EU-AIMS ADI-R Subtyping Connectivity Analysis
easypackages::libraries(c("here","ggplot2","nlme","readxl","matlabr","circlize","scico"))
source("/Users/mlombardo/Dropbox/GitHubRepos/utils/cohens_d.R")
source("/Users/mlombardo/Dropbox/R/Repfunctionspack6.R")
source("/Users/mlombardo/Dropbox/R/get_ggColorHue.R")
fdr_thresh = 0.05
options(matlab.path = "/Applications/MATLAB_R2019b.app/bin")
rootpath = "/Users/mlombardo/Dropbox/euaims/data/adir"
datapath = here("data")
codepath = here("code")
resultpath = here("results")
plotpath = here("plots")
Run the MATLAB script that estimates the partial correlations
RUNMATLAB = FALSE
if (RUNMATLAB){
# z = 0.5
code2run = sprintf("cd %s; estimateConnectivity_z05('%s',0,1);",codepath,"ridge")
res = run_matlab_code(code2run)
# z = 0.6
code2run = sprintf("cd %s; estimateConnectivity_z06('%s',0,1);",codepath,"ridge")
res = run_matlab_code(code2run)
# z = 0.7
code2run = sprintf("cd %s; estimateConnectivity_z07('%s',0,1);",codepath,"ridge")
res = run_matlab_code(code2run)
# z = 0.8
code2run = sprintf("cd %s; estimateConnectivity_z08('%s',0,1);",codepath,"ridge")
res = run_matlab_code(code2run)
# z = 0.9
code2run = sprintf("cd %s; estimateConnectivity_z09('%s',0,1);",codepath,"ridge")
res = run_matlab_code(code2run)
# z = 1
code2run = sprintf("cd %s; estimateConnectivity_z1('%s',0,1);",codepath,"ridge")
res = run_matlab_code(code2run)
}
Main analysis - Z = 0.5
# Z threshold
z_thresh = 0.5
fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))
fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")
tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4
asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
"B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
"A_pct_severity","B_pct_severity","z_ds")],
df,
by="subid")
vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))
asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")
#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE
if (RUNANALYSIS==TRUE) {
# columns with connectivity data
vars2use = colnames(df)[10:ncol(df)]
cnames = c("compNames",
"SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval",
"SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
"SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es",
"SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
"SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval",
"SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
"SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
"SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
"SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
"SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
"SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
"SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
colnames(aovres) = cnames
rownames(aovres) = vars2use
aovres$compNames = vars2use
vars2loop = c(1:length(vars2use))
for (i in vars2loop) {
y_var = vars2use[i]
# run analyses on Discovery and Replication datasets
df_Disc = subset(df, df$dataset=="Discovery")
df_Rep = subset(df, df$dataset=="Replication")
#--------------------------------------------------------------------------
# Discovery
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Disc,
na.action = na.omit)))
df_Disc$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)
# DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
# aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
# aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
# df_Disc$data2plot[df_Disc$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC
#--------------------------------------------------------------------------
# Replication
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Rep,
na.action = na.omit)))
df_Rep$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)
DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)
# DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
# aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
# aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
# df_Rep$data2plot[df_Rep$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC
current_state = sprintf("Loop %d",i)
fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
#--------------------------------------------------------------------------
# compute replication Bayes Factors
res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic,
trep = SCequalRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic,
trep = SCoverRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_over_RRB"),
n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
# # print("RRBoverSC")
# res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic,
# trep = RRBoverSC_vs_TD_Rep_statistic,
# n1 = sum(df_Disc$subgrp=="RRB_over_SC"),
# n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
# m1 = sum(df_Disc$subgrp=="TD"),
# m2 = sum(df_Rep$subgrp=="TD"),
# sample = 2,
# Type = 'ALL')
# aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic,
trep = SCequalRRB_vs_SCoverRRB_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="SC_over_RRB"),
m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
}
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
print(aovres[mask_allBF,])
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
} else {
fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
aovres = read_excel(fname)
}
## compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC06 IC01_IC06 1.8413429 0.067533513
## IC01_IC17 IC01_IC17 1.5666812 0.119282487
## IC03_IC12 IC03_IC12 2.8401768 0.005131071
## IC03_IC13 IC03_IC13 2.2147343 0.028276159
## IC07_IC13 IC07_IC13 -2.8083624 0.005637955
## IC08_IC13 IC08_IC13 -0.3378935 0.735912773
## IC12_IC17 IC12_IC17 1.2111391 0.227734188
## IC13_IC14 IC13_IC14 -1.8206843 0.070634676
## IC14_IC16 IC14_IC16 -3.1379365 0.002046295
## SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC06 -0.31589407 44.16421
## IC01_IC17 -0.31085336 42.95287
## IC03_IC12 -0.52491355 36.32295
## IC03_IC13 -0.40328866 -66.73545
## IC07_IC13 0.46427323 -35.40795
## IC08_IC13 0.05698519 -82.82008
## IC12_IC17 -0.22269681 53.46934
## IC13_IC14 0.32622066 -183.60601
## IC14_IC16 0.59459741 -186.70432
## SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC06 62.42476 2.65289954
## IC01_IC17 61.21342 2.18232049
## IC03_IC12 54.58350 2.82566015
## IC03_IC13 -48.47490 1.43933204
## IC07_IC13 -17.14740 -2.97423234
## IC08_IC13 -64.55952 -0.08929899
## IC12_IC17 71.72989 1.03087246
## IC13_IC14 -165.34546 -2.32057814
## IC14_IC16 -168.44377 -2.43350707
## SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC06 0.008789920 -0.442202657
## IC01_IC17 0.030553203 -0.378509720
## IC03_IC12 0.005323903 -0.495574986
## IC03_IC13 0.152022265 -0.274770515
## IC07_IC13 0.003395328 0.528531223
## IC08_IC13 0.928956676 0.009370899
## IC12_IC17 0.304166174 -0.185987687
## IC13_IC14 0.021580205 0.391666635
## IC14_IC16 0.016061402 0.410320007
## SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC06 59.560245 78.12275 19.4590668
## IC01_IC17 9.116484 27.67898 6.8012016
## IC03_IC12 33.917730 52.48023 33.8497301
## IC03_IC13 -131.336237 -112.77374 1.5748951
## IC07_IC13 -97.179641 -78.61714 51.4605481
## IC08_IC13 -114.633554 -96.07105 0.6747666
## IC12_IC17 26.210240 44.77274 1.1459067
## IC13_IC14 -167.216098 -148.65360 9.4304914
## IC14_IC16 -171.385723 -152.82322 10.4387817
## SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC06 0.7572340 0.449842834
## IC01_IC17 0.4593805 0.646483354
## IC03_IC12 2.0187698 0.044909445
## IC03_IC13 2.6093897 0.009788873
## IC07_IC13 -1.6891351 0.092825290
## IC08_IC13 1.1404445 0.255529170
## IC12_IC17 2.3199351 0.021401223
## IC13_IC14 -2.0447737 0.042249033
## IC14_IC16 -2.4362432 0.015757921
## SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC06 -0.08383745 15.16530
## IC01_IC17 -0.05621161 90.02013
## IC03_IC12 -0.28401316 50.42322
## IC03_IC13 -0.36665348 -105.62195
## IC07_IC13 0.22091396 -58.55765
## IC08_IC13 -0.17852219 -110.99064
## IC12_IC17 -0.34180496 56.50485
## IC13_IC14 0.29431425 -234.25081
## IC14_IC16 0.33880867 -236.85212
## SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC06 34.80330 -0.2030723
## IC01_IC17 109.65813 3.0806995
## IC03_IC12 70.06122 1.2376225
## IC03_IC13 -85.98396 2.7158706
## IC07_IC13 -38.91965 -3.5377902
## IC08_IC13 -91.35264 2.6455114
## IC12_IC17 76.14285 3.4929224
## IC13_IC14 -214.61282 -2.5268818
## IC14_IC16 -217.21412 -2.0457879
## SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC06 0.8392902313 0.03609126
## IC01_IC17 0.0023635282 -0.40852891
## IC03_IC12 0.2173442205 -0.17653043
## IC03_IC13 0.0072039804 -0.38221938
## IC07_IC13 0.0005042745 0.49892313
## IC08_IC13 0.0088220325 -0.37715315
## IC12_IC17 0.0005910705 -0.46184760
## IC13_IC14 0.0123022871 0.36008469
## IC14_IC16 0.0421198533 0.30492918
## SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC06 64.847220 84.60705 0.5685076
## IC01_IC17 22.430173 42.19000 13.8433937
## IC03_IC12 8.525016 28.28485 1.2856932
## IC03_IC13 -162.616305 -142.85648 26.4684802
## IC07_IC13 -90.490429 -70.73060 137.8846483
## IC08_IC13 -104.859512 -85.09968 12.8698565
## IC12_IC17 18.490888 38.25072 194.6394824
## IC13_IC14 -233.776752 -214.01692 15.5978769
## IC14_IC16 -220.270275 -200.51045 5.3511454
## SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC06 0.96044794 0.3389555
## IC01_IC17 0.96148792 0.3384350
## IC03_IC12 1.11565258 0.2670245
## IC03_IC13 0.07808261 0.9379055
## IC07_IC13 -0.99012090 0.3243079
## IC08_IC13 -1.11081930 0.2690905
## IC12_IC17 -0.61611145 0.5391055
## IC13_IC14 -0.22061138 0.8258077
## IC14_IC16 -1.31139157 0.1924812
## SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC06 -0.22890262 52.107351
## IC01_IC17 -0.20401823 84.797584
## IC03_IC12 -0.21724045 49.153699
## IC03_IC13 -0.04431897 -29.145006
## IC07_IC13 0.23597736 -9.320828
## IC08_IC13 0.22505132 -36.378068
## IC12_IC17 0.12293432 42.704912
## IC13_IC14 0.02991108 -156.737603
## IC14_IC16 0.23486204 -117.257104
## SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC06 68.418344 2.70460822
## IC01_IC17 101.108577 -0.43809266
## IC03_IC12 65.464693 1.71860081
## IC03_IC13 -12.834013 -0.60853590
## IC07_IC13 6.990165 0.04114173
## IC08_IC13 -20.067075 -2.11013008
## IC12_IC17 59.015905 -1.45310873
## IC13_IC14 -140.426610 0.01430202
## IC14_IC16 -100.946110 -0.42565084
## SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC06 0.007841698 -0.52670919
## IC01_IC17 0.662113323 0.05297288
## IC03_IC12 0.088287598 -0.29435833
## IC03_IC13 0.543991934 0.11211109
## IC07_IC13 0.967251859 -0.01689075
## IC08_IC13 0.036941747 0.38391122
## IC12_IC17 0.148825533 0.25602360
## IC13_IC14 0.988612979 0.05172973
## IC14_IC16 0.671131108 0.11437242
## SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC06 30.41902 47.243145
## IC01_IC17 25.04445 41.868580
## IC03_IC12 61.70028 78.524406
## IC03_IC13 -78.68936 -61.865238
## IC07_IC13 -19.04316 -2.219033
## IC08_IC13 -48.05103 -31.226908
## IC12_IC17 43.39973 60.223854
## IC13_IC14 -139.32191 -122.497785
## IC14_IC16 -139.51822 -122.694091
## SCequalRRB_vs_SCoverRRB.repBF
## IC01_IC06 12.6815102
## IC01_IC17 0.4576832
## IC03_IC12 2.7826665
## IC03_IC13 0.7371487
## IC07_IC13 0.5170103
## IC08_IC13 5.0438772
## IC12_IC17 1.6917758
## IC13_IC14 0.6741652
## IC14_IC16 0.6009812
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
## compNames SCequalRRB.repBF SCoverRRB.repBF
## IC01_IC06 IC01_IC06 19.4590668 0.5685076
## IC01_IC17 IC01_IC17 6.8012016 13.8433937
## IC03_IC12 IC03_IC12 33.8497301 1.2856932
## IC03_IC13 IC03_IC13 1.5748951 26.4684802
## IC07_IC13 IC07_IC13 51.4605481 137.8846483
## IC08_IC13 IC08_IC13 0.6747666 12.8698565
## IC12_IC17 IC12_IC17 1.1459067 194.6394824
## IC13_IC14 IC13_IC14 9.4304914 15.5978769
## IC14_IC16 IC14_IC16 10.4387817 5.3511454
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)
SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0
SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0
SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0
SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0
for (comp_pair in aovres$compNames){
comp1 = substr(comp_pair,1,4)
comp2 = substr(comp_pair,6,10)
if (aovres[comp_pair,"SCequalRRB.repBF"]>10 &
aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
} else{
SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"SCoverRRB.repBF"]>10 &
aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
} else{
SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
}
}
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
geom_tile(aes(fill=freq, alpha=0.5)) +
scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
theme_minimal() +
theme(legend.title = element_blank(),
legend.position = "none",
axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank()) +
coord_flip()
p_cbar
Main analysis - Z = 0.6
# Z threshold
z_thresh = 0.6
fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))
fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")
tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4
asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
"B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
"A_pct_severity","B_pct_severity","z_ds")],
df,
by="subid")
vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))
asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")
#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE
if (RUNANALYSIS==TRUE) {
# columns with connectivity data
vars2use = colnames(df)[10:ncol(df)]
cnames = c("compNames",
"SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval",
"SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
"SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es",
"SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
"SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval",
"SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
"SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
"SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
"SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
"SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
"SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
"SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
colnames(aovres) = cnames
rownames(aovres) = vars2use
aovres$compNames = vars2use
vars2loop = c(1:length(vars2use))
for (i in vars2loop) {
y_var = vars2use[i]
# run analyses on Discovery and Replication datasets
df_Disc = subset(df, df$dataset=="Discovery")
df_Rep = subset(df, df$dataset=="Replication")
#--------------------------------------------------------------------------
# Discovery
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Disc,
na.action = na.omit)))
df_Disc$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)
# DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
# aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
# aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
# df_Disc$data2plot[df_Disc$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC
#--------------------------------------------------------------------------
# Replication
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Rep,
na.action = na.omit)))
df_Rep$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)
DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)
# DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
# aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
# aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
# df_Rep$data2plot[df_Rep$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC
current_state = sprintf("Loop %d",i)
fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
#--------------------------------------------------------------------------
# compute replication Bayes Factors
res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic,
trep = SCequalRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic,
trep = SCoverRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_over_RRB"),
n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
# # print("RRBoverSC")
# res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic,
# trep = RRBoverSC_vs_TD_Rep_statistic,
# n1 = sum(df_Disc$subgrp=="RRB_over_SC"),
# n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
# m1 = sum(df_Disc$subgrp=="TD"),
# m2 = sum(df_Rep$subgrp=="TD"),
# sample = 2,
# Type = 'ALL')
# aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic,
trep = SCequalRRB_vs_SCoverRRB_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="SC_over_RRB"),
m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
}
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
print(aovres[mask_allBF,])
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
} else {
fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
aovres = read_excel(fname)
}
## compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC17 IC01_IC17 0.81720629 0.414988961
## IC03_IC13 IC03_IC13 1.90127132 0.059009568
## IC05_IC06 IC05_IC06 -0.84777062 0.397793817
## IC07_IC13 IC07_IC13 -2.84450243 0.005011569
## IC08_IC13 IC08_IC13 0.37624584 0.707217480
## IC12_IC17 IC12_IC17 0.78248573 0.435050702
## IC13_IC14 IC13_IC14 -1.77833953 0.077189666
## IC14_IC20 IC14_IC20 -0.01674859 0.986657422
## IC17_IC18 IC17_IC18 2.87509709 0.004571807
## SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC17 -0.146474183 53.93181
## IC03_IC13 -0.305211994 -82.08286
## IC05_IC06 0.140469299 142.61675
## IC07_IC13 0.442163838 -46.02786
## IC08_IC13 -0.063945163 -92.92388
## IC12_IC17 -0.128015783 63.64917
## IC13_IC14 0.276833395 -206.62311
## IC14_IC20 0.005368302 -174.10229
## IC17_IC18 -0.462014386 63.09198
## SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC17 72.71120 1.96934450
## IC03_IC13 -63.30346 1.71410891
## IC05_IC06 161.39614 -2.14851748
## IC07_IC13 -27.24847 -2.93772770
## IC08_IC13 -74.14449 -0.08528237
## IC12_IC17 82.42856 0.95503909
## IC13_IC14 -187.84372 -2.75453695
## IC14_IC20 -155.32290 2.05358856
## IC17_IC18 81.87137 1.85656428
## SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC17 0.050557979 -0.309488139
## IC03_IC13 0.088352994 -0.307641538
## IC05_IC06 0.033104923 0.338954646
## IC07_IC13 0.003770877 0.479576719
## IC08_IC13 0.932138454 0.002715119
## IC12_IC17 0.340930126 -0.175577118
## IC13_IC14 0.006525459 0.436367512
## IC14_IC20 0.041565450 -0.314145999
## IC17_IC18 0.065124293 -0.286659721
## SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC17 17.74967 36.63463 3.4984836
## IC03_IC13 -128.19376 -109.30879 2.9950345
## IC05_IC06 113.59895 132.48392 4.5945424
## IC07_IC13 -96.57675 -77.69178 47.8441467
## IC08_IC13 -112.97042 -94.08546 0.6678915
## IC12_IC17 23.51318 42.39815 1.1006862
## IC13_IC14 -181.97684 -163.09187 23.3826440
## IC14_IC20 -179.30575 -160.42079 1.9888481
## IC17_IC18 38.58658 57.47155 2.9514062
## SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC17 0.67056488 0.503343146
## IC03_IC13 2.92464289 0.003884712
## IC05_IC06 -0.02340494 0.981352762
## IC07_IC13 -1.65273052 0.100100326
## IC08_IC13 0.82752646 0.409016539
## IC12_IC17 2.22369981 0.027392033
## IC13_IC14 -2.23469322 0.026647323
## IC14_IC20 2.06411072 0.040418172
## IC17_IC18 3.11360312 0.002145382
## SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC17 -0.101073385 84.90317
## IC03_IC13 -0.425695383 -100.37980
## IC05_IC06 0.007493489 129.15594
## IC07_IC13 0.208090428 -54.16707
## IC08_IC13 -0.124665164 -106.96793
## IC12_IC17 -0.335209138 60.23513
## IC13_IC14 0.330565711 -220.17134
## IC14_IC20 -0.318094765 -229.81040
## IC17_IC18 -0.456312568 49.62148
## SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC17 104.28982 2.997975
## IC03_IC13 -80.99315 2.917101
## IC05_IC06 148.54259 -3.265278
## IC07_IC13 -34.78042 -3.365334
## IC08_IC13 -87.58127 2.824398
## IC12_IC17 79.62178 3.620622
## IC13_IC14 -200.78469 -2.151561
## IC14_IC20 -210.42375 2.328255
## IC17_IC18 69.00813 2.816996
## SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC17 0.0030851856 -0.4152045
## IC03_IC13 0.0039634269 -0.4109193
## IC05_IC06 0.0012994901 0.4712827
## IC07_IC13 0.0009267979 0.5026489
## IC08_IC13 0.0052479840 -0.4119680
## IC12_IC17 0.0003778628 -0.4862891
## IC13_IC14 0.0327076560 0.3139276
## IC14_IC20 0.0209622206 -0.3289709
## IC17_IC18 0.0053653571 -0.3846336
## SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC17 26.57670 46.12168 15.663905
## IC03_IC13 -157.12288 -137.57791 45.530629
## IC05_IC06 126.96464 146.50961 10.005277
## IC07_IC13 -90.53141 -70.98644 88.357797
## IC08_IC13 -108.01371 -88.46874 13.635879
## IC12_IC17 17.82539 37.37036 261.142404
## IC13_IC14 -222.96394 -203.41897 6.906068
## IC14_IC20 -286.52698 -266.98201 10.108331
## IC17_IC18 34.43571 53.98068 33.526075
## SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC17 0.2291000 0.8191982
## IC03_IC13 -0.6874310 0.4931952
## IC05_IC06 -0.7896305 0.4313687
## IC07_IC13 -1.1931590 0.2352629
## IC08_IC13 -0.3676063 0.7138425
## IC12_IC17 -1.0690293 0.2872952
## IC13_IC14 0.3335995 0.7392893
## IC14_IC20 -1.4795578 0.1417255
## IC17_IC18 0.1320008 0.8952143
## SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC17 -0.03143307 87.83246
## IC03_IC13 0.11255443 -38.73921
## IC05_IC06 0.13762120 104.91304
## IC07_IC13 0.22470100 -15.54993
## IC08_IC13 0.05857350 -43.07923
## IC12_IC17 0.20255134 56.69954
## IC13_IC14 -0.07479305 -166.27256
## IC14_IC20 0.27635916 -92.14238
## IC17_IC18 -0.02978529 11.97092
## SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC17 104.456568 -0.6936942
## IC03_IC13 -22.115098 -0.5383401
## IC05_IC06 121.537143 0.8769089
## IC07_IC13 1.074179 0.2070384
## IC08_IC13 -26.455127 -2.3133527
## IC12_IC17 73.323644 -1.6693701
## IC13_IC14 -149.648449 -0.5669362
## IC14_IC20 -75.518276 0.1377518
## IC17_IC18 28.595025 -0.4742845
## SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC17 0.48920358 0.11397366
## IC03_IC13 0.59133121 0.09498840
## IC05_IC06 0.38227381 -0.13381778
## IC07_IC13 0.83632789 -0.04431073
## IC08_IC13 0.02239135 0.40635080
## IC12_IC17 0.09762934 0.29086760
## IC13_IC14 0.57180775 0.14438115
## IC14_IC20 0.89066554 -0.04543209
## IC17_IC18 0.63615184 0.09394860
## SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC17 36.53677 53.458457
## IC03_IC13 -70.34723 -53.425542
## IC05_IC06 107.92575 124.847441
## IC07_IC13 -19.26034 -2.338646
## IC08_IC13 -51.02409 -34.102400
## IC12_IC17 42.32059 59.242281
## IC13_IC14 -143.39561 -126.473918
## IC14_IC20 -106.13772 -89.216032
## IC17_IC18 44.09157 61.013262
## SCequalRRB_vs_SCoverRRB.repBF
## IC01_IC17 0.7214381
## IC03_IC13 0.8030154
## IC05_IC06 0.5145181
## IC07_IC13 0.4340872
## IC08_IC13 3.9177261
## IC12_IC17 2.5739990
## IC13_IC14 0.6712763
## IC14_IC20 0.3627529
## IC17_IC18 0.7152729
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
## compNames SCequalRRB.repBF SCoverRRB.repBF
## IC01_IC17 IC01_IC17 3.4984836 15.663905
## IC03_IC13 IC03_IC13 2.9950345 45.530629
## IC05_IC06 IC05_IC06 4.5945424 10.005277
## IC07_IC13 IC07_IC13 47.8441467 88.357797
## IC08_IC13 IC08_IC13 0.6678915 13.635879
## IC12_IC17 IC12_IC17 1.1006862 261.142404
## IC13_IC14 IC13_IC14 23.3826440 6.906068
## IC14_IC20 IC14_IC20 1.9888481 10.108331
## IC17_IC18 IC17_IC18 2.9514062 33.526075
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)
SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0
SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0
SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0
SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0
for (comp_pair in aovres$compNames){
comp1 = substr(comp_pair,1,4)
comp2 = substr(comp_pair,6,10)
if (aovres[comp_pair,"SCequalRRB.repBF"]>10 &
aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
} else{
SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"SCoverRRB.repBF"]>10 &
aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
} else{
SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
}
}
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
geom_tile(aes(fill=freq, alpha=0.5)) +
scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
theme_minimal() +
theme(legend.title = element_blank(),
legend.position = "none",
axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank()) +
coord_flip()
p_cbar
Main analysis - Z = 0.7
# Z threshold
z_thresh = 0.7
fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))
fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")
tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4
asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
"B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
"A_pct_severity","B_pct_severity","z_ds")],
df,
by="subid")
vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))
asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")
#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE
if (RUNANALYSIS==TRUE) {
# columns with connectivity data
vars2use = colnames(df)[10:ncol(df)]
cnames = c("compNames",
"SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval",
"SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
"SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es",
"SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
"SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval",
"SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
"SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
"SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
"SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
"SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
"SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
"SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
colnames(aovres) = cnames
rownames(aovres) = vars2use
aovres$compNames = vars2use
vars2loop = c(1:length(vars2use))
for (i in vars2loop) {
y_var = vars2use[i]
# run analyses on Discovery and Replication datasets
df_Disc = subset(df, df$dataset=="Discovery")
df_Rep = subset(df, df$dataset=="Replication")
#--------------------------------------------------------------------------
# Discovery
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Disc,
na.action = na.omit)))
df_Disc$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)
# DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
# aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
# aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
# df_Disc$data2plot[df_Disc$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC
#--------------------------------------------------------------------------
# Replication
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Rep,
na.action = na.omit)))
df_Rep$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)
DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)
# DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
# aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
# aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
# df_Rep$data2plot[df_Rep$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC
current_state = sprintf("Loop %d",i)
fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
#--------------------------------------------------------------------------
# compute replication Bayes Factors
res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic,
trep = SCequalRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic,
trep = SCoverRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_over_RRB"),
n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
# # print("RRBoverSC")
# res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic,
# trep = RRBoverSC_vs_TD_Rep_statistic,
# n1 = sum(df_Disc$subgrp=="RRB_over_SC"),
# n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
# m1 = sum(df_Disc$subgrp=="TD"),
# m2 = sum(df_Rep$subgrp=="TD"),
# sample = 2,
# Type = 'ALL')
# aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic,
trep = SCequalRRB_vs_SCoverRRB_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="SC_over_RRB"),
m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
}
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
print(aovres[mask_allBF,])
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
} else {
fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
aovres = read_excel(fname)
}
## compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC03_IC12 IC03_IC12 2.5880777 0.010476625
## IC03_IC13 IC03_IC13 1.7045387 0.090086122
## IC07_IC13 IC07_IC13 -3.0190862 0.002921424
## IC12_IC17 IC12_IC17 0.8639704 0.388807371
## IC13_IC14 IC13_IC14 -1.9487761 0.052949098
## IC17_IC18 IC17_IC18 3.0694379 0.002492678
## SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC03_IC12 -0.4064357 32.02269
## IC03_IC13 -0.2684743 -91.14486
## IC07_IC13 0.4513105 -48.05953
## IC12_IC17 -0.1358028 59.83675
## IC13_IC14 0.2965627 -221.20416
## IC17_IC18 -0.4755712 60.01583
## SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC03_IC12 51.04559 2.5755098
## IC03_IC13 -72.12196 1.8029660
## IC07_IC13 -29.03662 -3.2756600
## IC12_IC17 78.85965 0.9077748
## IC13_IC14 -202.18126 -2.9243534
## IC17_IC18 79.03874 1.9834594
## SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC03_IC12 0.010830672 -0.4022660
## IC03_IC13 0.073104214 -0.3102543
## IC07_IC13 0.001269619 0.5095740
## IC12_IC17 0.365238501 -0.1634115
## IC13_IC14 0.003906066 0.4467737
## IC17_IC18 0.048871280 -0.2993575
## SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC03_IC12 28.13199 47.28974 18.381117
## IC03_IC13 -142.87159 -123.71385 3.522744
## IC07_IC13 -106.48364 -87.32590 129.143599
## IC12_IC17 26.27354 45.43128 1.058759
## IC13_IC14 -198.61959 -179.46185 37.428061
## IC17_IC18 39.19986 58.35761 3.596258
## SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC03_IC12 1.874730 0.062477220
## IC03_IC13 3.109391 0.002185226
## IC07_IC13 -1.377252 0.170173646
## IC12_IC17 2.169294 0.031393854
## IC13_IC14 -2.260699 0.024995687
## IC17_IC18 2.919683 0.003959615
## SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC03_IC12 -0.2859527 55.55774
## IC03_IC13 -0.4665540 -93.48004
## IC07_IC13 0.1782971 -53.80151
## IC12_IC17 -0.3350856 63.48068
## IC13_IC14 0.3417837 -206.30434
## IC17_IC18 -0.4457264 52.25844
## SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC03_IC12 74.74872 1.444407
## IC03_IC13 -74.28906 2.780506
## IC07_IC13 -34.61053 -3.053138
## IC12_IC17 82.67166 3.562849
## IC13_IC14 -187.11336 -1.854706
## IC17_IC18 71.44942 2.486167
## SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC03_IC12 0.1503440011 -0.2113858
## IC03_IC13 0.0059982978 -0.4022988
## IC07_IC13 0.0026041380 0.4708069
## IC12_IC17 0.0004684346 -0.5011120
## IC13_IC14 0.0652558885 0.2808212
## IC17_IC18 0.0138140386 -0.3457352
## SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC03_IC12 15.58339 34.93787 1.876880
## IC03_IC13 -145.79806 -126.44358 30.097648
## IC07_IC13 -84.18131 -64.82683 34.851302
## IC12_IC17 16.40916 35.76364 214.801944
## IC13_IC14 -210.19769 -190.84320 3.667604
## IC17_IC18 36.47313 55.82761 13.912669
## SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC03_IC12 0.5792453 0.5635469
## IC03_IC13 -1.1442064 0.2548936
## IC07_IC13 -1.3271076 0.1870793
## IC12_IC17 -1.0771983 0.2836268
## IC13_IC14 0.2587339 0.7962991
## IC17_IC18 0.1335787 0.8939672
## SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC03_IC12 -0.09518514 49.72572
## IC03_IC13 0.19476585 -41.15012
## IC07_IC13 0.26781460 -17.22303
## IC12_IC17 0.20065504 56.26102
## IC13_IC14 -0.07483254 -164.91591
## IC17_IC18 -0.04763199 10.89440
## SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC03_IC12 66.4004560 1.14189559
## IC03_IC13 -24.4753827 -0.50949046
## IC07_IC13 -0.5482864 -0.08776844
## IC12_IC17 72.9357568 -1.75752310
## IC13_IC14 -148.2411724 -0.85599717
## IC17_IC18 27.5691412 -0.07494163
## SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC03_IC12 0.25571552 -0.17433072
## IC03_IC13 0.61132136 0.09533090
## IC07_IC13 0.93020345 0.00370345
## IC12_IC17 0.08131659 0.31020269
## IC13_IC14 0.39366383 0.17936131
## IC17_IC18 0.94038290 0.04383111
## SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC03_IC12 62.47247 79.490164
## IC03_IC13 -73.47254 -56.454847
## IC07_IC13 -21.93991 -4.922215
## IC12_IC17 43.21120 60.228892
## IC13_IC14 -147.24087 -130.223174
## IC17_IC18 45.73968 62.757368
## SCequalRRB_vs_SCoverRRB.repBF
## IC03_IC12 1.2440002
## IC03_IC13 0.7123752
## IC07_IC13 0.4706535
## IC12_IC17 2.9153373
## IC13_IC14 0.7414271
## IC17_IC18 0.6905883
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
## compNames SCequalRRB.repBF SCoverRRB.repBF
## IC03_IC12 IC03_IC12 18.381117 1.876880
## IC03_IC13 IC03_IC13 3.522744 30.097648
## IC07_IC13 IC07_IC13 129.143599 34.851302
## IC12_IC17 IC12_IC17 1.058759 214.801944
## IC13_IC14 IC13_IC14 37.428061 3.667604
## IC17_IC18 IC17_IC18 3.596258 13.912669
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)
SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0
SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0
SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0
SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0
for (comp_pair in aovres$compNames){
comp1 = substr(comp_pair,1,4)
comp2 = substr(comp_pair,6,10)
if (aovres[comp_pair,"SCequalRRB.repBF"]>10 &
aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
} else{
SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"SCoverRRB.repBF"]>10 &
aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
} else{
SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
}
}
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
geom_tile(aes(fill=freq, alpha=0.5)) +
scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
theme_minimal() +
theme(legend.title = element_blank(),
legend.position = "none",
axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank()) +
coord_flip()
p_cbar
Main analysis - Z = 0.8
# Z threshold
z_thresh = 0.8
fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))
fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")
tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4
asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
"B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
"A_pct_severity","B_pct_severity","z_ds")],
df,
by="subid")
vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))
asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")
#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE
if (RUNANALYSIS==TRUE) {
# columns with connectivity data
vars2use = colnames(df)[10:ncol(df)]
cnames = c("compNames",
"SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval",
"SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
"SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es",
"SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
"SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval",
"SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
"SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
"SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
"SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
"SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
"SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
"SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
colnames(aovres) = cnames
rownames(aovres) = vars2use
aovres$compNames = vars2use
vars2loop = c(1:length(vars2use))
for (i in vars2loop) {
y_var = vars2use[i]
# run analyses on Discovery and Replication datasets
df_Disc = subset(df, df$dataset=="Discovery")
df_Rep = subset(df, df$dataset=="Replication")
#--------------------------------------------------------------------------
# Discovery
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Disc,
na.action = na.omit)))
df_Disc$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)
# DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
# aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
# aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
# df_Disc$data2plot[df_Disc$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC
#--------------------------------------------------------------------------
# Replication
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Rep,
na.action = na.omit)))
df_Rep$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)
DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)
# DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
# aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
# aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
# df_Rep$data2plot[df_Rep$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC
current_state = sprintf("Loop %d",i)
fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
#--------------------------------------------------------------------------
# compute replication Bayes Factors
res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic,
trep = SCequalRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic,
trep = SCoverRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_over_RRB"),
n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
# # print("RRBoverSC")
# res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic,
# trep = RRBoverSC_vs_TD_Rep_statistic,
# n1 = sum(df_Disc$subgrp=="RRB_over_SC"),
# n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
# m1 = sum(df_Disc$subgrp=="TD"),
# m2 = sum(df_Rep$subgrp=="TD"),
# sample = 2,
# Type = 'ALL')
# aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic,
trep = SCequalRRB_vs_SCoverRRB_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="SC_over_RRB"),
m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
}
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
print(aovres[mask_allBF,])
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
} else {
fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
aovres = read_excel(fname)
}
## compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC17 IC01_IC17 1.0305727 0.304131713
## IC03_IC13 IC03_IC13 2.1060507 0.036594355
## IC04_IC12 IC04_IC12 1.8287645 0.069099104
## IC05_IC06 IC05_IC06 -1.4268750 0.155357909
## IC07_IC13 IC07_IC13 -2.7311666 0.006942746
## IC12_IC17 IC12_IC17 1.0942124 0.275332092
## IC13_IC14 IC13_IC14 -1.8336087 0.068372806
## IC14_IC20 IC14_IC20 0.7908874 0.430056350
## SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC17 -0.1696165 53.54754
## IC03_IC13 -0.3197982 -74.69340
## IC04_IC12 -0.2894145 11.74338
## IC05_IC06 0.2182136 151.71066
## IC07_IC13 0.3961681 -53.53290
## IC12_IC17 -0.1681853 60.31799
## IC13_IC14 0.2647979 -225.76200
## IC14_IC20 -0.1202411 -185.59447
## SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC17 72.80446 2.484744
## IC03_IC13 -55.43648 1.681717
## IC04_IC12 31.00030 2.510147
## IC05_IC06 170.96758 -3.245194
## IC07_IC13 -34.27598 -3.277802
## IC12_IC17 79.57491 1.678583
## IC13_IC14 -206.50508 -3.092962
## IC14_IC20 -166.33756 2.828282
## SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC17 0.013857552 -0.3585327
## IC03_IC13 0.094320199 -0.2781578
## IC04_IC12 0.012930524 -0.3882510
## IC05_IC06 0.001394337 0.4737234
## IC07_IC13 0.001250767 0.4842942
## IC12_IC17 0.094930889 -0.2627585
## IC13_IC14 0.002290504 0.4553751
## IC14_IC20 0.005198638 -0.4122374
## SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC17 10.46885 29.88750 8.947798
## IC03_IC13 -152.75374 -133.33509 2.710811
## IC04_IC12 35.61025 55.02890 14.166801
## IC05_IC06 122.45496 141.87361 55.278160
## IC07_IC13 -110.07476 -90.65611 124.496029
## IC12_IC17 37.16924 56.58789 2.638261
## IC13_IC14 -212.73468 -193.31602 52.718034
## IC14_IC20 -207.00345 -187.58480 13.203380
## SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC17 0.5745338 0.566357330
## IC03_IC13 2.9108858 0.004081391
## IC04_IC12 1.6609264 0.098549997
## IC05_IC06 0.3323600 0.740022157
## IC07_IC13 -1.4093308 0.160542822
## IC12_IC17 2.0739640 0.039572574
## IC13_IC14 -2.5201979 0.012639440
## IC14_IC20 1.8422945 0.067153762
## SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC17 -0.08592597 83.88668
## IC03_IC13 -0.44202806 -110.41566
## IC04_IC12 -0.27042699 35.65420
## IC05_IC06 -0.04922147 122.55064
## IC07_IC13 0.19549205 -49.95789
## IC12_IC17 -0.32713065 63.22810
## IC13_IC14 0.39608797 -201.79719
## IC14_IC20 -0.29742196 -209.45733
## SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC17 102.90959 2.8165131
## IC03_IC13 -91.39276 2.9764795
## IC04_IC12 54.67710 0.7078691
## IC05_IC06 141.57355 -2.4150774
## IC07_IC13 -30.93499 -3.0735101
## IC12_IC17 82.25101 3.0003029
## IC13_IC14 -182.77429 -1.4592095
## IC14_IC20 -190.43443 1.3235037
## SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC17 0.005415889 -0.4036258
## IC03_IC13 0.003331189 -0.4519289
## IC04_IC12 0.479973687 -0.1081443
## IC05_IC06 0.016767411 0.3688586
## IC07_IC13 0.002456547 0.4957857
## IC12_IC17 0.003093274 -0.4294027
## IC13_IC14 0.146310369 0.2508351
## IC14_IC20 0.187404268 -0.1787898
## SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC17 32.477196 51.56790 10.0904147
## IC03_IC13 -135.660751 -116.57005 53.8616685
## IC04_IC12 39.816621 58.90732 0.7189301
## IC05_IC06 118.699433 137.79013 1.9417081
## IC07_IC13 -80.749737 -61.65904 36.5368325
## IC12_IC17 8.067035 27.15774 47.1011394
## IC13_IC14 -197.526971 -178.43627 1.5288431
## IC14_IC20 -265.976212 -246.88551 1.5745783
## SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC17 0.32503496 0.7457302
## IC03_IC13 -0.52325690 0.6017767
## IC04_IC12 -0.08604668 0.9315751
## IC05_IC06 -1.56088713 0.1212284
## IC07_IC13 -0.86791084 0.3872048
## IC12_IC17 -0.86107252 0.3909444
## IC13_IC14 0.76137705 0.4479508
## IC14_IC20 -0.74009670 0.4607113
## SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC17 -0.064703223 87.17469
## IC03_IC13 0.077583422 -39.54237
## IC04_IC12 0.001098146 65.89069
## IC05_IC06 0.271559564 107.49471
## IC07_IC13 0.194510437 -18.70223
## IC12_IC17 0.161252814 56.10897
## IC13_IC14 -0.164650852 -164.04131
## IC14_IC20 0.137933498 -86.35061
## SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC17 103.949435 -0.4064373
## IC03_IC13 -22.767626 -1.1193206
## IC04_IC12 82.665438 1.4889595
## IC05_IC06 124.269452 -0.4394489
## IC07_IC13 -1.927483 0.1752804
## IC12_IC17 72.883713 -0.8136384
## IC13_IC14 -147.266566 -0.9481650
## IC14_IC20 -69.575865 1.2629225
## SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC17 0.6851276 0.06560365
## IC03_IC13 0.2651845 0.17729569
## IC04_IC12 0.1390576 -0.26653269
## IC05_IC06 0.6611074 0.09833275
## IC07_IC13 0.8611475 -0.03507937
## IC12_IC17 0.4174244 0.13005861
## IC13_IC14 0.3449046 0.21466746
## IC14_IC20 0.2090059 -0.25099662
## SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC17 36.04966 53.067348
## IC03_IC13 -74.43356 -57.415871
## IC04_IC12 46.61123 63.628918
## IC05_IC06 108.56816 125.585852
## IC07_IC13 -21.95994 -4.942246
## IC12_IC17 45.60554 62.623236
## IC13_IC14 -147.40800 -130.390312
## IC14_IC20 -111.94447 -94.926782
## SCequalRRB_vs_SCoverRRB.repBF
## IC01_IC17 0.6659977
## IC03_IC13 1.2055291
## IC04_IC12 1.1518490
## IC05_IC06 0.5579912
## IC07_IC13 0.5401290
## IC12_IC17 0.9734609
## IC13_IC14 0.5297640
## IC14_IC20 0.5750721
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
## compNames SCequalRRB.repBF SCoverRRB.repBF
## IC01_IC17 IC01_IC17 8.947798 10.0904147
## IC03_IC13 IC03_IC13 2.710811 53.8616685
## IC04_IC12 IC04_IC12 14.166801 0.7189301
## IC05_IC06 IC05_IC06 55.278160 1.9417081
## IC07_IC13 IC07_IC13 124.496029 36.5368325
## IC12_IC17 IC12_IC17 2.638261 47.1011394
## IC13_IC14 IC13_IC14 52.718034 1.5288431
## IC14_IC20 IC14_IC20 13.203380 1.5745783
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)
SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0
SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0
SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0
SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0
for (comp_pair in aovres$compNames){
comp1 = substr(comp_pair,1,4)
comp2 = substr(comp_pair,6,10)
if (aovres[comp_pair,"SCequalRRB.repBF"]>10 &
aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
} else{
SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"SCoverRRB.repBF"]>10 &
aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
} else{
SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
}
}
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
geom_tile(aes(fill=freq, alpha=0.5)) +
scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
theme_minimal() +
theme(legend.title = element_blank(),
legend.position = "none",
axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank()) +
coord_flip()
p_cbar
Main analysis - Z = 0.9
# Z threshold
z_thresh = 0.9
fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))
fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")
tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4
asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
"B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
"A_pct_severity","B_pct_severity","z_ds")],
df,
by="subid")
vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))
asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")
#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE
if (RUNANALYSIS==TRUE) {
# columns with connectivity data
vars2use = colnames(df)[10:ncol(df)]
cnames = c("compNames",
"SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval",
"SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
"SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es",
"SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
"SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval",
"SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
"SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
"SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
"SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
"SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
"SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
"SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
colnames(aovres) = cnames
rownames(aovres) = vars2use
aovres$compNames = vars2use
vars2loop = c(1:length(vars2use))
for (i in vars2loop) {
y_var = vars2use[i]
# run analyses on Discovery and Replication datasets
df_Disc = subset(df, df$dataset=="Discovery")
df_Rep = subset(df, df$dataset=="Replication")
#--------------------------------------------------------------------------
# Discovery
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Disc,
na.action = na.omit)))
df_Disc$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)
# DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
# aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
# aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
# df_Disc$data2plot[df_Disc$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC
#--------------------------------------------------------------------------
# Replication
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Rep,
na.action = na.omit)))
df_Rep$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)
DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)
# DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
# aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
# aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
# df_Rep$data2plot[df_Rep$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC
current_state = sprintf("Loop %d",i)
fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
#--------------------------------------------------------------------------
# compute replication Bayes Factors
res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic,
trep = SCequalRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic,
trep = SCoverRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_over_RRB"),
n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
# # print("RRBoverSC")
# res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic,
# trep = RRBoverSC_vs_TD_Rep_statistic,
# n1 = sum(df_Disc$subgrp=="RRB_over_SC"),
# n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
# m1 = sum(df_Disc$subgrp=="TD"),
# m2 = sum(df_Rep$subgrp=="TD"),
# sample = 2,
# Type = 'ALL')
# aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic,
trep = SCequalRRB_vs_SCoverRRB_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="SC_over_RRB"),
m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
}
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
print(aovres[mask_allBF,])
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
} else {
fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
aovres = read_excel(fname)
}
## compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC03_IC12 IC03_IC12 2.9500548 0.0035833145
## IC03_IC13 IC03_IC13 2.1886429 0.0298617971
## IC04_IC12 IC04_IC12 1.7055493 0.0897524456
## IC05_IC06 IC05_IC06 -1.1831531 0.2382504425
## IC07_IC13 IC07_IC13 -2.6225681 0.0094464192
## IC12_IC17 IC12_IC17 0.9793108 0.3286916675
## IC13_IC14 IC13_IC14 -2.2274790 0.0271077812
## IC17_IC18 IC17_IC18 3.4313864 0.0007391005
## IC18_IC19 IC18_IC19 -0.3978081 0.6912254051
## SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC03_IC12 -0.41989167 26.32983
## IC03_IC13 -0.32159384 -81.27676
## IC04_IC12 -0.25519631 16.34733
## IC05_IC06 0.17081776 154.99511
## IC07_IC13 0.36367254 -59.83515
## IC12_IC17 -0.14372368 60.37815
## IC13_IC14 0.30807244 -242.35592
## IC17_IC18 -0.49179659 54.27725
## IC18_IC19 0.07086403 -67.89787
## SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC03_IC12 45.84347 2.467422
## IC03_IC13 -61.76312 2.118814
## IC04_IC12 35.86097 2.827196
## IC05_IC06 174.50875 -3.314643
## IC07_IC13 -40.32151 -3.757203
## IC12_IC17 79.89179 2.102288
## IC13_IC14 -222.84228 -3.486615
## IC17_IC18 73.79089 2.776003
## IC18_IC19 -48.38423 2.274385
## SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC03_IC12 0.0144800741 -0.3557387
## IC03_IC13 0.0353850843 -0.3285241
## IC04_IC12 0.0051907316 -0.4172130
## IC05_IC06 0.0010959585 0.4690847
## IC07_IC13 0.0002274984 0.5359627
## IC12_IC17 0.0368250095 -0.3062489
## IC13_IC14 0.0006056142 0.4896397
## IC17_IC18 0.0060450096 -0.3973893
## IC18_IC19 0.0240428334 -0.3196829
## SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC03_IC12 30.74456 50.443779 13.143527
## IC03_IC13 -164.94723 -145.248008 6.453284
## IC04_IC12 30.94980 50.649019 26.837898
## IC05_IC06 124.99396 144.693185 51.333840
## IC07_IC13 -114.61304 -94.913817 494.617934
## IC12_IC17 36.36942 56.068644 4.663503
## IC13_IC14 -229.38628 -209.687058 183.316277
## IC17_IC18 35.54869 55.247908 27.231818
## IC18_IC19 -13.06937 6.629851 1.572227
## SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC03_IC12 1.5486383 0.123384585
## IC03_IC13 3.0124184 0.003000278
## IC04_IC12 1.8739535 0.062704826
## IC05_IC06 0.1792632 0.857950954
## IC07_IC13 -1.5946374 0.112707022
## IC12_IC17 2.3077226 0.022256419
## IC13_IC14 -2.1807901 0.030614638
## IC17_IC18 2.5468056 0.011785942
## IC18_IC19 1.5264319 0.128816934
## SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC03_IC12 -0.25456546 58.61301
## IC03_IC13 -0.48229966 -103.90343
## IC04_IC12 -0.32726059 30.19700
## IC05_IC06 -0.02874286 120.77866
## IC07_IC13 0.23465455 -42.99462
## IC12_IC17 -0.39147562 61.15969
## IC13_IC14 0.36512361 -187.92639
## IC17_IC18 -0.41784998 56.10796
## IC18_IC19 -0.26790057 -86.29745
## SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC03_IC12 77.39240 1.4917369
## IC03_IC13 -85.12404 2.6324914
## IC04_IC12 48.97639 0.2360359
## IC05_IC06 139.55806 -2.2560310
## IC07_IC13 -24.21523 -2.4630636
## IC12_IC17 79.93908 2.5253906
## IC13_IC14 -169.14700 -0.8457121
## IC17_IC18 74.88735 1.4035518
## IC18_IC19 -67.51806 2.4132771
## SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC03_IC12 0.137666087 -0.2346498
## IC03_IC13 0.009275937 -0.4077152
## IC04_IC12 0.813695759 -0.0456331
## IC05_IC06 0.025374147 0.3696337
## IC07_IC13 0.014796084 0.4171082
## IC12_IC17 0.012494058 -0.3786487
## IC13_IC14 0.398930761 0.1767343
## IC17_IC18 0.162320244 -0.2047941
## IC18_IC19 0.016898289 -0.3952378
## SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC03_IC12 12.924087 31.73888 2.1396824
## IC03_IC13 -125.688943 -106.87415 20.5529595
## IC04_IC12 42.286263 61.10105 0.3724043
## IC05_IC06 115.648680 134.46347 2.0120331
## IC07_IC13 -77.174491 -58.35970 11.6745316
## IC12_IC17 8.817891 27.63268 16.1654706
## IC13_IC14 -185.519379 -166.70459 0.6475302
## IC17_IC18 38.304652 57.11944 1.3624388
## IC18_IC19 -52.317823 -33.50303 10.3020900
## SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC03_IC12 0.7483015 0.45575471
## IC03_IC13 -0.7283203 0.46784831
## IC04_IC12 -0.5205385 0.60365544
## IC05_IC06 -1.0809649 0.28189853
## IC07_IC13 -0.5515502 0.58229075
## IC12_IC17 -1.4359984 0.15362636
## IC13_IC14 0.5512722 0.58248069
## IC17_IC18 0.3923431 0.69550659
## IC18_IC19 -1.6853271 0.09454611
## SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC03_IC12 -0.13283149 48.07414
## IC03_IC13 0.11417045 -39.86271
## IC04_IC12 0.07251492 65.23453
## IC05_IC06 0.20206808 108.87030
## IC07_IC13 0.11976008 -18.07354
## IC12_IC17 0.25097355 53.94688
## IC13_IC14 -0.08989251 -166.37880
## IC17_IC18 -0.08380033 9.88557
## IC18_IC19 0.29376949 -10.82918
## SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC03_IC12 64.898268 0.7078715
## IC03_IC13 -23.038586 -0.6125566
## IC04_IC12 82.058652 1.9526977
## IC05_IC06 125.694429 -0.4304303
## IC07_IC13 -1.249410 -0.4736645
## IC12_IC17 70.771011 -0.3381107
## IC13_IC14 -149.554671 -1.3752581
## IC17_IC18 26.709696 1.0116727
## IC18_IC19 5.994947 -0.1755206
## SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC03_IC12 0.48035346 -0.11954417
## IC03_IC13 0.54129130 0.09230374
## IC04_IC12 0.05310846 -0.35214621
## IC05_IC06 0.66762985 0.09133783
## IC07_IC13 0.63657203 0.08676581
## IC12_IC17 0.73585154 0.04183277
## IC13_IC14 0.17153098 0.32756894
## IC17_IC18 0.31366459 -0.18365873
## IC18_IC19 0.86095691 0.02214878
## SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC03_IC12 63.42217 80.487292
## IC03_IC13 -75.51437 -58.449244
## IC04_IC12 45.36737 62.432493
## IC05_IC06 108.33571 125.400830
## IC07_IC13 -23.56616 -6.501035
## IC12_IC17 45.17502 62.240144
## IC13_IC14 -150.72817 -133.663049
## IC17_IC18 43.86876 60.933887
## IC18_IC19 37.79721 54.862332
## SCequalRRB_vs_SCoverRRB.repBF
## IC03_IC12 0.9030264
## IC03_IC13 0.8441854
## IC04_IC12 1.0275350
## IC05_IC06 0.6893732
## IC07_IC13 0.7842290
## IC12_IC17 0.5459025
## IC13_IC14 0.7187953
## IC17_IC18 1.0674748
## IC18_IC19 0.3993259
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4
aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
## compNames SCequalRRB.repBF SCoverRRB.repBF
## IC03_IC12 IC03_IC12 13.143527 2.1396824
## IC03_IC13 IC03_IC13 6.453284 20.5529595
## IC04_IC12 IC04_IC12 26.837898 0.3724043
## IC05_IC06 IC05_IC06 51.333840 2.0120331
## IC07_IC13 IC07_IC13 494.617934 11.6745316
## IC12_IC17 IC12_IC17 4.663503 16.1654706
## IC13_IC14 IC13_IC14 183.316277 0.6475302
## IC17_IC18 IC17_IC18 27.231818 1.3624388
## IC18_IC19 IC18_IC19 1.572227 10.3020900
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)
SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0
SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0
SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0
SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0
for (comp_pair in aovres$compNames){
comp1 = substr(comp_pair,1,4)
comp2 = substr(comp_pair,6,10)
if (aovres[comp_pair,"SCequalRRB.repBF"]>10 &
aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
} else{
SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"SCoverRRB.repBF"]>10 &
aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
} else{
SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
}
}
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
geom_tile(aes(fill=freq, alpha=0.5)) +
scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
theme_minimal() +
theme(legend.title = element_blank(),
legend.position = "none",
axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank()) +
coord_flip()
p_cbar
Main analysis - Z = 1
# Z threshold
z_thresh = 1
fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))
fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")
tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4
tmp_df$AB_pct_severity = tmp_df$A_pct_severity + tmp_df$B_pct_severity
asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
"B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
"A_pct_severity","B_pct_severity","AB_pct_severity","z_ds")],
df,
by="subid")
vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))
asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")
#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE
if (RUNANALYSIS==TRUE) {
# columns with connectivity data
vars2use = colnames(df)[10:ncol(df)]
cnames = c("compNames",
"SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval",
"SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
"SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es",
"SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
"SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval",
"SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
"SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
"SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
"SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
"SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC",
"SCequalRRB_Disc_vs_SCoverRRB.BIC",
"SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval",
"SCequalRRB_Rep_vs_SCoverRRB.es",
"SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC",
"SCequalRRB_vs_SCoverRRB.repBF",
"SCcorr_Disc.r","SCcorr_Disc.t","SCcorr_Disc.pval",
"SCcorr_Rep.r","SCcorr_Rep.t","SCcorr_Rep.pval","SCcorr.repBF",
"RRBcorr_Disc.r","RRBcorr_Disc.t","RRBcorr_Disc.pval",
"RRBcorr_Rep.r","RRBcorr_Rep.t","RRBcorr_Rep.pval","RRBcorr.repBF",
"SumSCRRB_Disc.r","SumSCRRB_Disc.t","SumSCRRB_Disc.pval",
"SumSCRRB_Rep.r","SumSCRRB_Rep.t","SumSCRRB_Rep.pval","SumSCRRB.repBF",
"zds_Disc.r","zds_Disc.t","zds_Disc.pval",
"zds_Rep.r","zds_Rep.t","zds_Rep.pval","zds.repBF",
"SumSCRRB_SCequalRRB_Disc.r","SumSCRRB_SCequalRRB_Disc.t","SumSCRRB_SCequalRRB_Disc.pval",
"SumSCRRB_SCequalRRB_Rep.r","SumSCRRB_SCequalRRB_Rep.t",
"SumSCRRB_SCequalRRB_Rep.pval","SumSCRRB_SCequalRRB.repBF",
"zds_SCequalRRB_Disc.r","zds_SCequalRRB_Disc.t","zds_SCequalRRB_Disc.pval",
"zds_SCequalRRB_Rep.r","zds_SCequalRRB_Rep.t","zds_SCequalRRB_Rep.pval","zds_SCequalRRB.repBF",
"zds_SCoverRRB_Disc.r","zds_SCoverRRB_Disc.t","zds_SCoverRRB_Disc.pval",
"zds_SCoverRRB_Rep.r","zds_SCoverRRB_Rep.t","zds_SCoverRRB_Rep.pval","zds_SCoverRRB.repBF",
"VinelandABC_Disc.r","VinelandABC_Disc.t","VinelandABC_Disc.pval",
"VinelandABC_Rep.r","VinelandABC_Rep.t",
"VinelandABC_Rep.pval","VinelandABC.repBF",
"VinelandABC_SCequalRRB_Disc.r","VinelandABC_SCequalRRB_Disc.t","VinelandABC_SCequalRRB_Disc.pval",
"VinelandABC_SCequalRRB_Rep.r","VinelandABC_SCequalRRB_Rep.t",
"VinelandABC_SCequalRRB_Rep.pval","VinelandABC_SCequalRRB.repBF",
"VinelandABC_SCoverRRB_Disc.r","VinelandABC_SCoverRRB_Disc.t","VinelandABC_SCoverRRB_Disc.pval",
"VinelandABC_SCoverRRB_Rep.r","VinelandABC_SCoverRRB_Rep.t",
"VinelandABC_SCoverRRB_Rep.pval","VinelandABC_SCoverRRB.repBF")
# "vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard"
aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
colnames(aovres) = cnames
rownames(aovres) = vars2use
aovres$compNames = vars2use
vars2loop = c(1:length(vars2use))
for (i in vars2loop) {
y_var = vars2use[i]
# run analyses on Discovery and Replication datasets
df_Disc = subset(df, df$dataset=="Discovery")
df_Rep = subset(df, df$dataset=="Replication")
#--------------------------------------------------------------------------
# Discovery
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Disc,
na.action = na.omit)))
df_Disc$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)
# DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
# aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
# aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
# df_Disc$data2plot[df_Disc$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
# aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC
#--------------------------------------------------------------------------
DASD = subset(asd_df, asd_df$dataset=="Discovery")
DASD$site = factor(DASD$site)
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"A_pct_severity","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"SCcorr_Disc.t"] = res$tTable["A_pct_severity","t-value"]
aovres[y_var,"SCcorr_Disc.pval"] = res$tTable["A_pct_severity","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"A_pct_severity","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ A_pct_severity + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$A_pct_severity)
aovres[y_var,"SCcorr_Disc.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"B_pct_severity","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"RRBcorr_Disc.t"] = res$tTable["B_pct_severity","t-value"]
aovres[y_var,"RRBcorr_Disc.pval"] = res$tTable["B_pct_severity","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"B_pct_severity","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ B_pct_severity + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$B_pct_severity)
aovres[y_var,"RRBcorr_Disc.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"SumSCRRB_Disc.t"] = res$tTable["AB_pct_severity","t-value"]
aovres[y_var,"SumSCRRB_Disc.pval"] = res$tTable["AB_pct_severity","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ AB_pct_severity + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$AB_pct_severity)
aovres[y_var,"SumSCRRB_Disc.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"zds_Disc.t"] = res$tTable["z_ds","t-value"]
aovres[y_var,"zds_Disc.pval"] = res$tTable["z_ds","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$z_ds)
aovres[y_var,"zds_Disc.r"] = res$estimate
# Vineland ABC
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"VinelandABC_Disc.t"] = res$tTable["vabsabcabc_standard","t-value"]
aovres[y_var,"VinelandABC_Disc.pval"] = res$tTable["vabsabcabc_standard","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$vabsabcabc_standard)
aovres[y_var,"VinelandABC_Disc.r"] = res$estimate
DASD_SCequalRRB = subset(DASD,DASD$subgrp=="SC_equal_RRB")
DASD_SCoverRRB = subset(DASD,DASD$subgrp=="SC_over_RRB")
# Vineland ABC
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCequalRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"VinelandABC_SCequalRRB_Disc.t"] = res$tTable["vabsabcabc_standard","t-value"]
aovres[y_var,"VinelandABC_SCequalRRB_Disc.pval"] = res$tTable["vabsabcabc_standard","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD_SCequalRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$vabsabcabc_standard)
aovres[y_var,"VinelandABC_SCequalRRB_Disc.r"] = res$estimate
# Vineland ABC
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCoverRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"VinelandABC_SCoverRRB_Disc.t"] = res$tTable["vabsabcabc_standard","t-value"]
aovres[y_var,"VinelandABC_SCoverRRB_Disc.pval"] = res$tTable["vabsabcabc_standard","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCoverRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD_SCoverRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCoverRRB$covadj = as.numeric(t(DASD_SCoverRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCoverRRB$covadj,DASD_SCoverRRB$vabsabcabc_standard)
aovres[y_var,"VinelandABC_SCoverRRB_Disc.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCequalRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"SumSCRRB_SCequalRRB_Disc.t"] = res$tTable["AB_pct_severity","t-value"]
aovres[y_var,"SumSCRRB_SCequalRRB_Disc.pval"] = res$tTable["AB_pct_severity","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ AB_pct_severity + sex + scan_age + site, data=DASD_SCequalRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$AB_pct_severity)
aovres[y_var,"SumSCRRB_SCequalRRB_Disc.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCequalRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"zds_SCequalRRB_Disc.t"] = res$tTable["z_ds","t-value"]
aovres[y_var,"zds_SCequalRRB_Disc.pval"] = res$tTable["z_ds","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD_SCequalRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$z_ds)
aovres[y_var,"zds_SCequalRRB_Disc.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCoverRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"zds_SCoverRRB_Disc.t"] = res$tTable["z_ds","t-value"]
aovres[y_var,"zds_SCoverRRB_Disc.pval"] = res$tTable["z_ds","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCoverRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD_SCoverRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCoverRRB$covadj = as.numeric(t(DASD_SCoverRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCoverRRB$covadj,DASD_SCoverRRB$z_ds)
aovres[y_var,"zds_SCoverRRB_Disc.r"] = res$estimate
# res = cor.test(DASD[,"A_pct_severity"],DASD[,y_var])
# aovres[y_var,"SCcorr_Disc.r"] = res$estimate
# aovres[y_var,"SCcorr_Disc.pval"] = res$p.value
# res = cor.test(DASD[,"B_pct_severity"],DASD[,y_var])
# aovres[y_var,"RRBcorr_Disc.r"] = res$estimate
# aovres[y_var,"RRBcorr_Disc.pval"] = res$p.value
# res = cor.test(DASD[,"AB_pct_severity"],DASD[,y_var])
# aovres[y_var,"SumSCRRB_Disc.r"] = res$estimate
# aovres[y_var,"SumSCRRB_Disc.pval"] = res$p.value
n_orig = dim(DASD)[1]
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
# Replication
# grab residuals after accounting for sex and scan_age
fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = df_Rep,
na.action = na.omit)))
df_Rep$data2plot = resid(mod2use)
# compute t-stats
fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
rx_form = as.formula(sprintf("~ 1|%s","site"))
DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD1,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)
DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD2,
na.action = na.omit)))
res = summary(mod2use)
SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)
# DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
# mod2use = eval(substitute(lme(fixed = fx_form,
# random = rx_form,
# data = DASD3,
# na.action = na.omit)))
# res = summary(mod2use)
# RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
# RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
# RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
# RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
mod2use = eval(substitute(lme(fixed = fx_form,
random = rx_form,
data = DASD4,
na.action = na.omit)))
res = summary(mod2use)
SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)
aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="TD"])
aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
# aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
# aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
# df_Rep$data2plot[df_Rep$subgrp=="TD"])
# aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
# aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC
#--------------------------------------------------------------------------
DASD = subset(asd_df, asd_df$dataset=="Replication")
DASD$site = factor(DASD$site)
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"A_pct_severity","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"SCcorr_Rep.t"] = res$tTable["A_pct_severity","t-value"]
aovres[y_var,"SCcorr_Rep.pval"] = res$tTable["A_pct_severity","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"A_pct_severity","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ A_pct_severity + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$A_pct_severity)
aovres[y_var,"SCcorr_Rep.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"B_pct_severity","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"RRBcorr_Rep.t"] = res$tTable["B_pct_severity","t-value"]
aovres[y_var,"RRBcorr_Rep.pval"] = res$tTable["B_pct_severity","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"B_pct_severity","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ B_pct_severity + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$B_pct_severity)
aovres[y_var,"RRBcorr_Rep.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"SumSCRRB_Rep.t"] = res$tTable["AB_pct_severity","t-value"]
aovres[y_var,"SumSCRRB_Rep.pval"] = res$tTable["AB_pct_severity","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ AB_pct_severity + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$AB_pct_severity)
aovres[y_var,"SumSCRRB_Rep.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"zds_Rep.t"] = res$tTable["z_ds","t-value"]
aovres[y_var,"zds_Rep.pval"] = res$tTable["z_ds","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$z_ds)
aovres[y_var,"zds_Rep.r"] = res$estimate
# Vineland ABC
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"VinelandABC_Rep.t"] = res$tTable["vabsabcabc_standard","t-value"]
aovres[y_var,"VinelandABC_Rep.pval"] = res$tTable["vabsabcabc_standard","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD$covadj,DASD$vabsabcabc_standard)
aovres[y_var,"VinelandABC_Rep.r"] = res$estimate
DASD_SCequalRRB = subset(DASD,DASD$subgrp=="SC_equal_RRB")
DASD_SCoverRRB = subset(DASD,DASD$subgrp=="SC_over_RRB")
# Vineland ABC
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCequalRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"VinelandABC_SCequalRRB_Rep.t"] = res$tTable["vabsabcabc_standard","t-value"]
aovres[y_var,"VinelandABC_SCequalRRB_Rep.pval"] = res$tTable["vabsabcabc_standard","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD_SCequalRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$vabsabcabc_standard)
aovres[y_var,"VinelandABC_SCequalRRB_Rep.r"] = res$estimate
# Vineland ABC
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCoverRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"VinelandABC_SCoverRRB_Rep.t"] = res$tTable["vabsabcabc_standard","t-value"]
aovres[y_var,"VinelandABC_SCoverRRB_Rep.pval"] = res$tTable["vabsabcabc_standard","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCoverRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD_SCoverRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCoverRRB$covadj = as.numeric(t(DASD_SCoverRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCoverRRB$covadj,DASD_SCoverRRB$vabsabcabc_standard)
aovres[y_var,"VinelandABC_SCoverRRB_Rep.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCequalRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"SumSCRRB_SCequalRRB_Rep.t"] = res$tTable["AB_pct_severity","t-value"]
aovres[y_var,"SumSCRRB_SCequalRRB_Rep.pval"] = res$tTable["AB_pct_severity","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ AB_pct_severity + sex + scan_age + site, data=DASD_SCequalRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$AB_pct_severity)
aovres[y_var,"SumSCRRB_SCequalRRB_Rep.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCequalRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"zds_SCequalRRB_Rep.t"] = res$tTable["z_ds","t-value"]
aovres[y_var,"zds_SCequalRRB_Rep.pval"] = res$tTable["z_ds","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD_SCequalRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$z_ds)
aovres[y_var,"zds_SCequalRRB_Rep.r"] = res$estimate
fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
mod2use = eval(substitute(lme(fixed = fx_form2,
random = rx_form,
data = DASD_SCoverRRB,
na.action = na.omit)))
res = summary(mod2use)
aovres[y_var,"zds_SCoverRRB_Rep.t"] = res$tTable["z_ds","t-value"]
aovres[y_var,"zds_SCoverRRB_Rep.pval"] = res$tTable["z_ds","p-value"]
lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
lm_mod = lm(formula=lm_form, data = DASD_SCoverRRB, na.action = na.omit)
covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
beta1[is.na(beta1)] = 0
full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD_SCoverRRB)
covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
DASD_SCoverRRB$covadj = as.numeric(t(DASD_SCoverRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
res = cor.test(DASD_SCoverRRB$covadj,DASD_SCoverRRB$z_ds)
aovres[y_var,"zds_SCoverRRB_Rep.r"] = res$estimate
# res = cor.test(DASD[,"A_pct_severity"],DASD[,y_var])
# aovres[y_var,"SCcorr_Rep.r"] = res$estimate
# aovres[y_var,"SCcorr_Rep.pval"] = res$p.value
# res = cor.test(DASD[,"B_pct_severity"],DASD[,y_var])
# aovres[y_var,"RRBcorr_Rep.r"] = res$estimate
# aovres[y_var,"RRBcorr_Rep.pval"] = res$p.value
# res = cor.test(DASD[,"AB_pct_severity"],DASD[,y_var])
# aovres[y_var,"SumSCRRB_Rep.r"] = res$estimate
# aovres[y_var,"SumSCRRB_Rep.pval"] = res$p.value
n_rep = dim(DASD)[1]
#--------------------------------------------------------------------------
current_state = sprintf("Loop %d",i)
fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
#--------------------------------------------------------------------------
# compute replication Bayes Factors
res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic,
trep = SCequalRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic,
trep = SCoverRRB_vs_TD_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_over_RRB"),
n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
m1 = sum(df_Disc$subgrp=="TD"),
m2 = sum(df_Rep$subgrp=="TD"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
# # print("RRBoverSC")
# res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic,
# trep = RRBoverSC_vs_TD_Rep_statistic,
# n1 = sum(df_Disc$subgrp=="RRB_over_SC"),
# n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
# m1 = sum(df_Disc$subgrp=="TD"),
# m2 = sum(df_Rep$subgrp=="TD"),
# sample = 2,
# Type = 'ALL')
# aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic,
trep = SCequalRRB_vs_SCoverRRB_Rep_statistic,
n1 = sum(df_Disc$subgrp=="SC_equal_RRB"),
n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
m1 = sum(df_Disc$subgrp=="SC_over_RRB"),
m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
sample = 2,
Type = 'ALL')
aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]
#--------------------------------------------------------------------------
res_bf = BFSALL(tobs =aovres[y_var,"SCcorr_Disc.t"],
trep = aovres[y_var,"SCcorr_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"SCcorr.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"RRBcorr_Disc.t"],
trep = aovres[y_var,"RRBcorr_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"RRBcorr.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"SumSCRRB_Disc.t"],
trep = aovres[y_var,"SumSCRRB_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"SumSCRRB.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"zds_Disc.t"],
trep = aovres[y_var,"zds_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"zds.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"VinelandABC_Disc.t"],
trep = aovres[y_var,"VinelandABC_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"VinelandABC.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"SumSCRRB_SCequalRRB_Disc.t"],
trep = aovres[y_var,"SumSCRRB_SCequalRRB_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"SumSCRRB_SCequalRRB.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"zds_SCequalRRB_Disc.t"],
trep = aovres[y_var,"zds_SCequalRRB_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"zds_SCequalRRB.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"zds_SCoverRRB_Disc.t"],
trep = aovres[y_var,"zds_SCoverRRB_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"zds_SCoverRRB.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"VinelandABC_SCequalRRB_Disc.t"],
trep = aovres[y_var,"VinelandABC_SCequalRRB_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"VinelandABC_SCequalRRB.repBF"] = res_bf["Replication BF","Replication 1"]
res_bf = BFSALL(tobs =aovres[y_var,"VinelandABC_SCoverRRB_Disc.t"],
trep = aovres[y_var,"VinelandABC_SCoverRRB_Rep.t"],
n1 = n_orig,
n2 = n_rep,
sample = 1,
Type = 'ALL')
aovres[y_var,"VinelandABC_SCoverRRB.repBF"] = res_bf["Replication BF","Replication 1"]
# res_bf = CorrelationReplicationBF(r.orig = aovres[y_var,"SCcorr_Disc.r"],
# n.orig = n_orig,
# r.rep = aovres[y_var,"SCcorr_Rep.r"],
# n.rep = n_rep)
# aovres[y_var,"SCcorr.repBF"] = res_bf["BF10"]
# res_bf = CorrelationReplicationBF(r.orig = aovres[y_var,"RRBcorr_Disc.r"],
# n.orig = n_orig,
# r.rep = aovres[y_var,"RRBcorr_Rep.r"],
# n.rep = n_rep)
# aovres[y_var,"RRBcorr.repBF"] = res_bf["BF10"]
# res_bf = CorrelationReplicationBF(r.orig = aovres[y_var,"SumSCRRB_Disc.r"],
# n.orig = n_orig,
# r.rep = aovres[y_var,"SumSCRRB_Rep.r"],
# n.rep = n_rep)
# aovres[y_var,"SumSCRRB.repBF"] = res_bf["BF10"]
#--------------------------------------------------------------------------
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
# write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
}
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask5 = aovres$SCcorr.repBF>=10
mask6 = aovres$RRBcorr.repBF>=10
mask7 = aovres$SumSCRRB.repBF>=10
mask8 = aovres$zds.repBF>=10
mask9 = aovres$zds_SCequalRRB.repBF>=10
mask10 = aovres$zds_SCoverRRB.repBF>=10
mask11 = aovres$SumSCRRB_SCequalRRB.repBF>=10
mask12 = aovres$VinelandABC.repBF>=10
mask13 = aovres$VinelandABC_SCequalRRB.repBF>=10
mask14 = aovres$VinelandABC_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4 | mask5 | mask6 | mask7 | mask8 | mask9 | mask10 | mask11 | mask12 | mask13 | mask14
print(aovres[mask_allBF,])
# save results to a file
fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
} else {
fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
aovres = read_excel(fname)
}
## compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC12 IC01_IC12 -0.2496273 0.8031410927
## IC03_IC13 IC03_IC13 2.2285209 0.0269997941
## IC04_IC11 IC04_IC11 -0.1484457 0.8821460000
## IC04_IC12 IC04_IC12 1.7940013 0.0743782660
## IC05_IC06 IC05_IC06 -1.2919451 0.1979210508
## IC05_IC19 IC05_IC19 0.9851454 0.3257860231
## IC07_IC13 IC07_IC13 -2.7387981 0.0067439048
## IC07_IC16 IC07_IC16 1.2745753 0.2039920282
## IC08_IC11 IC08_IC11 -0.3117874 0.7555386701
## IC08_IC20 IC08_IC20 -0.1264030 0.8995444968
## IC11_IC12 IC11_IC12 1.2946614 0.1969838128
## IC12_IC17 IC12_IC17 1.0821493 0.2805363825
## IC13_IC14 IC13_IC14 -2.3577628 0.0193861300
## IC14_IC20 IC14_IC20 1.3632092 0.1744047813
## IC15_IC17 IC15_IC17 -1.2965093 0.1963481239
## IC17_IC18 IC17_IC18 3.6411454 0.0003485864
## SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC12 0.03053444 -0.9200558
## IC03_IC13 -0.31893337 -90.0593434
## IC04_IC11 0.01274671 193.4339021
## IC04_IC12 -0.25554293 22.1627717
## IC05_IC06 0.18250921 154.8103250
## IC05_IC19 -0.13856428 136.2350990
## IC07_IC13 0.36930472 -65.6644763
## IC07_IC16 -0.18364899 74.2220213
## IC08_IC11 0.03386850 108.9647159
## IC08_IC20 0.02044076 -115.1178960
## IC11_IC12 -0.18820948 11.5947112
## IC12_IC17 -0.15667411 58.8410802
## IC13_IC14 0.31519898 -252.4141615
## IC14_IC20 -0.19509918 -211.5651943
## IC15_IC17 0.18949119 35.8945904
## IC17_IC18 -0.51483822 51.7198686
## SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC12 18.77917 -0.5435691
## IC03_IC13 -70.36012 2.4683205
## IC04_IC11 213.13312 0.6090771
## IC04_IC12 41.86199 2.5764811
## IC05_IC06 174.50955 -3.4648826
## IC05_IC19 155.93432 0.7017945
## IC07_IC13 -45.96525 -3.8668930
## IC07_IC16 93.92124 -0.8574564
## IC08_IC11 128.66394 0.6556000
## IC08_IC20 -95.41867 1.8104838
## IC11_IC12 31.29393 0.5271688
## IC12_IC17 78.54030 2.0453046
## IC13_IC14 -232.71494 -3.7317613
## IC14_IC20 -191.86597 2.5515062
## IC15_IC17 55.59381 0.1683646
## IC17_IC18 71.41909 2.6365540
## SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC12 0.5873464610 0.06816497
## IC03_IC13 0.0144183974 -0.37502204
## IC04_IC11 0.5431680402 -0.08596104
## IC04_IC12 0.0107052798 -0.37221036
## IC05_IC06 0.0006496279 0.48026006
## IC05_IC19 0.4836272797 -0.09183902
## IC07_IC13 0.0001492405 0.53888946
## IC07_IC16 0.3922241929 0.12285958
## IC08_IC11 0.5128387076 -0.09439621
## IC08_IC20 0.0717291247 -0.25879566
## IC11_IC12 0.5986635397 -0.08832568
## IC12_IC17 0.0421408659 -0.29104485
## IC13_IC14 0.0002480568 0.51583745
## IC14_IC20 0.0114769542 -0.34332152
## IC15_IC17 0.8664674609 -0.02669512
## IC17_IC18 0.0090361760 -0.36819744
## SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC12 -28.38384 -8.504608 0.7955174
## IC03_IC13 -170.54202 -150.662781 14.0854368
## IC04_IC11 213.64541 233.524647 0.7314191
## IC04_IC12 29.03154 48.910775 16.1957454
## IC05_IC06 123.82345 143.702690 80.0450180
## IC05_IC19 124.85020 144.729436 0.8767820
## IC07_IC13 -118.76241 -98.883173 737.3305480
## IC07_IC16 113.58642 133.465656 0.3237813
## IC08_IC11 74.34074 94.219976 0.6878668
## IC08_IC20 -90.43583 -70.556592 1.4262907
## IC11_IC12 -68.66126 -48.782025 0.6891131
## IC12_IC17 35.35480 55.234039 4.5013271
## IC13_IC14 -238.33387 -218.454631 393.9080616
## IC14_IC20 -217.49223 -197.612992 12.5596603
## IC15_IC17 82.58892 102.468155 0.4119129
## IC17_IC18 33.64229 53.521530 16.2852363
## SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC12 -1.4989340 0.135861199
## IC03_IC13 2.9664175 0.003475083
## IC04_IC11 -0.2685483 0.788623270
## IC04_IC12 1.6651903 0.097831688
## IC05_IC06 0.3473936 0.728752209
## IC05_IC19 0.8833145 0.378391662
## IC07_IC13 -1.5394266 0.125675522
## IC07_IC16 1.2146139 0.226302929
## IC08_IC11 -0.7203461 0.472362955
## IC08_IC20 0.3857991 0.700157941
## IC11_IC12 1.4575925 0.146912831
## IC12_IC17 2.1780533 0.030866033
## IC13_IC14 -2.1748957 0.031106239
## IC14_IC20 1.1054536 0.270622575
## IC15_IC17 -1.2588909 0.209903372
## IC17_IC18 2.4069749 0.017224053
## SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC12 0.23334387 -8.211494
## IC03_IC13 -0.49830010 -96.166936
## IC04_IC11 0.03303592 175.916360
## IC04_IC12 -0.31221826 25.172009
## IC05_IC06 -0.05644977 120.570260
## IC05_IC19 -0.14220895 113.755358
## IC07_IC13 0.23063765 -37.883659
## IC07_IC16 -0.18861642 113.526107
## IC08_IC11 0.15722966 103.191544
## IC08_IC20 -0.06295329 -56.327164
## IC11_IC12 -0.25226542 21.609438
## IC12_IC17 -0.38329438 62.741737
## IC13_IC14 0.38549480 -179.523638
## IC14_IC20 -0.23771320 -187.317672
## IC15_IC17 0.24070307 30.258310
## IC17_IC18 -0.40645726 59.316748
## SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC12 10.38770 -0.1911435
## IC03_IC13 -77.56774 2.2155916
## IC04_IC11 194.51556 0.7650052
## IC04_IC12 43.77121 0.4145599
## IC05_IC06 139.16946 -2.1094196
## IC05_IC19 132.35456 1.4531123
## IC07_IC13 -19.28446 -2.3883109
## IC07_IC16 132.12531 0.3470195
## IC08_IC11 121.79074 -0.2706070
## IC08_IC20 -37.72797 -0.2163795
## IC11_IC12 40.20864 2.5671076
## IC12_IC17 81.34094 2.3858770
## IC13_IC14 -160.92444 -0.4694337
## IC14_IC20 -168.71847 1.4884527
## IC15_IC17 48.85751 0.5090827
## IC17_IC18 77.91595 1.4006002
## SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC12 0.84865397 0.04046754
## IC03_IC13 0.02812249 -0.36583152
## IC04_IC11 0.44538832 -0.15597163
## IC04_IC12 0.67901597 -0.07585451
## IC05_IC06 0.03645500 0.36291603
## IC05_IC19 0.14813889 -0.25619906
## IC07_IC13 0.01808414 0.42431630
## IC07_IC16 0.72902985 -0.03451740
## IC08_IC11 0.78703994 0.05217370
## IC08_IC20 0.82896589 0.09501337
## IC11_IC12 0.01116553 -0.41918679
## IC12_IC17 0.01820003 -0.37987412
## IC13_IC14 0.63939478 0.12909039
## IC14_IC20 0.13858766 -0.23672652
## IC15_IC17 0.61139122 -0.03857520
## IC17_IC18 0.16325800 -0.21556482
## SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC12 -21.64452 -3.008849 0.4690143
## IC03_IC13 -120.59743 -101.961762 6.9485035
## IC04_IC11 205.05179 223.687466 0.7257347
## IC04_IC12 44.64199 63.277667 0.5209997
## IC05_IC06 116.35188 134.987555 1.4328289
## IC05_IC19 116.36092 134.996593 1.8687764
## IC07_IC13 -74.01734 -55.381662 9.8489366
## IC07_IC16 121.08427 139.719939 0.6219226
## IC08_IC11 68.85415 87.489827 0.6957798
## IC08_IC20 -98.79765 -80.161977 0.6610975
## IC11_IC12 -50.51656 -31.880887 13.3782336
## IC12_IC17 10.11203 28.747702 11.5978596
## IC13_IC14 -179.33802 -160.702346 0.3826019
## IC14_IC20 -266.49051 -247.854835 2.0574500
## IC15_IC17 66.72561 85.361279 0.3687863
## IC17_IC18 40.29085 58.926524 1.4573308
## SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC12 1.06562973 0.2887305
## IC03_IC13 -0.92747309 0.3555427
## IC04_IC11 0.07578615 0.9397154
## IC04_IC12 -0.41171568 0.6812823
## IC05_IC06 -1.27865794 0.2034849
## IC05_IC19 -0.06885305 0.9452212
## IC07_IC13 -0.56891543 0.5704768
## IC07_IC16 -0.13229628 0.8949715
## IC08_IC11 0.84130353 0.4018513
## IC08_IC20 -0.38838580 0.6984187
## IC11_IC12 -0.56107794 0.5757906
## IC12_IC17 -1.34367581 0.1815885
## IC13_IC14 0.70244557 0.4837617
## IC14_IC20 -0.19706352 0.8441112
## IC15_IC17 0.36487654 0.7158458
## IC17_IC18 0.66242596 0.5089685
## SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC12 -0.20786490 -2.749578
## IC03_IC13 0.14065762 -41.817613
## IC04_IC11 -0.02222239 122.504440
## IC04_IC12 0.05024782 65.322676
## IC05_IC06 0.24020480 108.451300
## IC05_IC19 0.00107773 144.261739
## IC07_IC13 0.12504641 -19.184422
## IC07_IC16 0.02789699 98.481402
## IC08_IC11 -0.12354558 86.852640
## IC08_IC20 0.07334816 -6.378542
## IC11_IC12 0.07451368 2.251464
## IC12_IC17 0.23336423 53.568095
## IC13_IC14 -0.10782769 -166.874716
## IC14_IC20 0.02781499 -89.164877
## IC15_IC17 -0.04716789 34.078033
## IC17_IC18 -0.11368454 11.006911
## SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC12 14.123528 -0.036769258
## IC03_IC13 -24.944507 -0.089286819
## IC04_IC11 139.377546 -0.694297702
## IC04_IC12 82.195782 1.588992840
## IC05_IC06 125.324406 -0.467778230
## IC05_IC19 161.134845 -0.771857298
## IC07_IC13 -2.311315 -0.500517418
## IC07_IC16 115.354508 -0.969364134
## IC08_IC11 103.725746 0.881997843
## IC08_IC20 10.494564 1.966233011
## IC11_IC12 19.124570 -1.546259600
## IC12_IC17 70.441202 -0.418141665
## IC13_IC14 -150.001610 -1.820714277
## IC14_IC20 -72.291771 0.713906068
## IC15_IC17 50.951139 0.008702881
## IC17_IC18 27.880017 0.853037598
## SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC12 0.97072764 0.028127395
## IC03_IC13 0.92899687 0.007794058
## IC04_IC11 0.48878391 0.089084628
## IC04_IC12 0.11458819 -0.276865834
## IC05_IC06 0.64075772 0.101191130
## IC05_IC19 0.44165573 0.141760026
## IC07_IC13 0.61759091 0.084157259
## IC07_IC16 0.33423545 0.165803830
## IC08_IC11 0.37947088 -0.136005123
## IC08_IC20 0.05148792 -0.311009261
## IC11_IC12 0.12456993 0.291036546
## IC12_IC17 0.67656101 0.058276698
## IC13_IC14 0.07104226 0.408021997
## IC14_IC20 0.47661617 -0.162348541
## IC15_IC17 0.99307007 0.011773670
## IC17_IC18 0.39527006 -0.142619611
## SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC12 -2.920641 1.419154e+01
## IC03_IC13 -75.964605 -5.885242e+01
## IC04_IC11 123.878930 1.409911e+02
## IC04_IC12 46.097716 6.320990e+01
## IC05_IC06 108.442552 1.255547e+02
## IC05_IC19 140.018742 1.571309e+02
## IC07_IC13 -24.551099 -7.438918e+00
## IC07_IC16 72.680178 8.979236e+01
## IC08_IC11 80.713383 9.782556e+01
## IC08_IC20 -17.109353 2.828336e-03
## IC11_IC12 3.424649 2.053683e+01
## IC12_IC17 45.612007 6.272419e+01
## IC13_IC14 -153.238002 -1.361258e+02
## IC14_IC20 -115.254969 -9.814279e+01
## IC15_IC17 51.604858 6.871704e+01
## IC17_IC18 44.245333 6.135751e+01
## SCequalRRB_vs_SCoverRRB.repBF SCcorr_Disc.r SCcorr_Disc.t
## IC01_IC12 0.5179629 0.02194196 0.42665054
## IC03_IC13 0.5894995 -0.21565303 -2.15128320
## IC04_IC11 0.7740118 0.05368490 0.96235535
## IC04_IC12 0.9164447 -0.04308262 -0.32111128
## IC05_IC06 0.6625575 -0.02776040 -0.54631033
## IC05_IC19 0.8396703 0.08944708 1.03701535
## IC07_IC13 0.7959574 0.02825143 0.25964948
## IC07_IC16 0.9472991 0.04398889 0.30052154
## IC08_IC11 1.0372300 0.11614033 1.60916326
## IC08_IC20 1.2178738 0.06102096 0.48328981
## IC11_IC12 1.8221373 -0.09722395 -0.95708854
## IC12_IC17 0.6164800 -0.09815559 -1.12468193
## IC13_IC14 0.7524868 -0.05987545 -0.39990554
## IC14_IC20 0.7378927 0.10943481 0.72915968
## IC15_IC17 0.6819499 0.03926970 0.07988827
## IC17_IC18 1.0046741 0.12777685 1.07803010
## SCcorr_Disc.pval SCcorr_Rep.r SCcorr_Rep.t SCcorr_Rep.pval
## IC01_IC12 0.6703983 0.003233085 0.15461485 0.87737433
## IC03_IC13 0.0334581 -0.126963611 -1.12300938 0.26358581
## IC04_IC11 0.3378064 -0.018351087 -0.18924902 0.85020459
## IC04_IC12 0.7486846 0.008786831 0.44549366 0.65673363
## IC05_IC06 0.5858669 0.009072133 -0.13993955 0.88893298
## IC05_IC19 0.3018139 -0.036191267 -0.34401951 0.73140964
## IC07_IC13 0.7955791 0.056376570 0.26710893 0.78982547
## IC07_IC16 0.7642994 -0.018554199 -0.19095518 0.84887051
## IC08_IC11 0.1102083 0.187959425 2.27255249 0.02476101
## IC08_IC20 0.6297706 0.200556463 2.05566773 0.04189683
## IC11_IC12 0.3404468 -0.124975082 -1.24159464 0.21671088
## IC12_IC17 0.2629685 -0.203831211 -2.31675788 0.02214415
## IC13_IC14 0.6899370 -0.016340357 0.07893432 0.93721109
## IC14_IC20 0.4673248 -0.034469408 -0.67167717 0.50302870
## IC15_IC17 0.9364592 -0.068463813 -1.08495884 0.28002795
## IC17_IC18 0.2831826 -0.097275106 -1.20345834 0.23107323
## SCcorr.repBF RRBcorr_Disc.r RRBcorr_Disc.t RRBcorr_Disc.pval
## IC01_IC12 0.6948175 0.022253885 0.41920559 0.67581534
## IC03_IC13 0.9932646 -0.100984042 -0.98383037 0.32717839
## IC04_IC11 0.5092501 0.001223994 0.85033233 0.39683381
## IC04_IC12 0.6679560 -0.018878034 -0.10172859 0.91914190
## IC05_IC06 0.6784365 0.129883126 1.02441277 0.30770101
## IC05_IC19 0.4593061 0.111258537 1.19479533 0.23452312
## IC07_IC13 0.7257912 0.006504303 0.17706303 0.85975739
## IC07_IC16 0.6716151 0.046963448 0.01189321 0.99053057
## IC08_IC11 8.0951621 0.140225485 1.70980028 0.08988736
## IC08_IC20 3.1180687 0.004305451 -0.25344479 0.80035822
## IC11_IC12 1.4839549 -0.065262479 -0.60958908 0.54328656
## IC12_IC17 7.0608340 0.268013929 2.12742673 0.03543136
## IC13_IC14 0.6627342 -0.033549747 -0.07222455 0.94254345
## IC14_IC20 0.5366952 0.059448943 0.13091575 0.89606118
## IC15_IC17 0.9027455 0.059630717 0.24894077 0.80383217
## IC17_IC18 0.3932325 0.066930613 0.21592829 0.82941025
## RRBcorr_Rep.r RRBcorr_Rep.t RRBcorr_Rep.pval RRBcorr.repBF
## IC01_IC12 -0.031570729 0.43793056 0.662192433 0.7714689
## IC03_IC13 -0.105545423 -0.97743952 0.330239493 1.1275916
## IC04_IC11 -0.006080479 0.69246450 0.489930085 0.8842338
## IC04_IC12 -0.132022831 -1.28323728 0.201782973 1.1336710
## IC05_IC06 0.008273737 0.01175331 0.990641170 0.5386826
## IC05_IC19 0.020045801 0.12026905 0.904463122 0.5254590
## IC07_IC13 0.080865498 0.37982124 0.704722665 0.7454881
## IC07_IC16 -0.065311241 -1.07680757 0.283640056 0.9349332
## IC08_IC11 0.234578549 2.30655588 0.022725465 8.8949014
## IC08_IC20 0.135383657 1.14996703 0.252352808 0.8340661
## IC11_IC12 -0.029488716 -0.56718002 0.571609043 0.8210033
## IC12_IC17 -0.111746204 -0.72156532 0.471909306 0.1191160
## IC13_IC14 0.240763309 2.83555055 0.005337477 4.5660604
## IC14_IC20 -0.196980403 -2.08910559 0.038726525 1.8112695
## IC15_IC17 0.181375760 1.51968554 0.131115427 1.4936044
## IC17_IC18 -0.131431759 -1.21259283 0.227572306 0.8830450
## SumSCRRB_Disc.r SumSCRRB_Disc.t SumSCRRB_Disc.pval SumSCRRB_Rep.r
## IC01_IC12 0.02901956 0.5310098 0.59639342 -0.01216495
## IC03_IC13 -0.21386179 -1.9781943 0.05019802 -0.14306998
## IC04_IC11 0.03906505 1.0959706 0.27528630 -0.01646374
## IC04_IC12 -0.04208466 -0.2700538 0.78758276 -0.05450550
## IC05_IC06 0.05825782 0.2156622 0.82961723 0.01057013
## IC05_IC19 0.13048683 1.3482337 0.18012260 -0.01768556
## IC07_IC13 0.02407416 0.2742754 0.78434455 0.07926642
## IC07_IC16 0.05957621 0.2202769 0.82602984 -0.04395750
## IC08_IC11 0.16676443 2.0117014 0.04649155 0.24768499
## IC08_IC20 0.04615055 0.1748611 0.86148346 0.21130671
## IC11_IC12 -0.10848355 -0.9888179 0.32474187 -0.10658917
## IC12_IC17 0.09204190 0.4721155 0.63770172 -0.20289733
## IC13_IC14 -0.06286582 -0.3133240 0.75457845 0.09966974
## IC14_IC20 0.11370131 0.5391124 0.59080811 -0.11684976
## IC15_IC17 0.06380923 0.2176714 0.82805483 0.03307955
## IC17_IC18 0.13122882 0.8776867 0.38186762 -0.13299202
## SumSCRRB_Rep.t SumSCRRB_Rep.pval SumSCRRB.repBF zds_Disc.r
## IC01_IC12 0.32693046 0.744267295 0.7302895 0.002657793
## IC03_IC13 -1.27302692 0.205371003 1.3723820 -0.107756293
## IC04_IC11 0.09844675 0.921735250 0.5458223 0.043233647
## IC04_IC12 -0.35513921 0.723083828 0.7446164 -0.022345781
## IC05_IC06 -0.09186411 0.926953069 0.6864205 -0.112569212
## IC05_IC19 -0.18477365 0.853706029 0.3920961 -0.003416208
## IC07_IC13 0.36927339 0.712548753 0.7487993 0.018705713
## IC07_IC16 -0.61417528 0.540215720 0.7123406 0.003703727
## IC08_IC11 2.70863823 0.007703423 22.7349100 -0.001563469
## IC08_IC20 2.02789026 0.044697063 2.3254318 0.047133160
## IC11_IC12 -1.12832858 0.261342137 1.3174932 -0.034806562
## IC12_IC17 -1.93457578 0.055301730 1.0794209 -0.266888039
## IC13_IC14 1.46765418 0.144709887 0.9382909 -0.026012431
## IC14_IC20 -1.47966288 0.141479102 0.7619345 0.048866197
## IC15_IC17 -0.03661856 0.970847557 0.6886295 -0.008920903
## IC17_IC18 -1.41513365 0.159515783 0.5152166 0.058791490
## zds_Disc.t zds_Disc.pval zds_Rep.r zds_Rep.t zds_Rep.pval
## IC01_IC12 0.02685358 0.978621128 0.023309478 -0.158594291 0.87424447
## IC03_IC13 -0.99853453 0.320029498 -0.062880426 -0.430408517 0.66763968
## IC04_IC11 0.15616824 0.876162830 -0.014899531 -0.587190628 0.55813481
## IC04_IC12 -0.19469984 0.845957187 0.092781160 1.438981648 0.15265506
## IC05_IC06 -1.30640519 0.193913203 0.004027948 -0.149721114 0.88122595
## IC05_IC19 -0.11501962 0.908621815 -0.049681652 -0.445095420 0.65702061
## IC07_IC13 0.08289200 0.934075525 0.006338976 -0.005453223 0.99565767
## IC07_IC16 0.27075993 0.787040843 0.022444169 0.503936006 0.61519338
## IC08_IC11 -0.04063034 0.967658120 0.042788914 0.643662229 0.52097367
## IC08_IC20 0.60851801 0.543994106 0.119371439 1.294230664 0.19797173
## IC11_IC12 -0.33824990 0.735765943 -0.109081175 -0.878497723 0.38135921
## IC12_IC17 -2.80788842 0.005822396 -0.137767387 -1.880084783 0.06242402
## IC13_IC14 -0.28190050 0.778505391 -0.170165849 -1.763193184 0.08031152
## IC14_IC20 0.54296251 0.588162711 0.090168120 0.851331964 0.39621295
## IC15_IC17 -0.11415196 0.909308101 -0.185111864 -2.076675164 0.03988013
## IC17_IC18 0.73741185 0.462311380 -0.015996310 -0.353999369 0.72393577
## zds.repBF SumSCRRB_SCequalRRB_Disc.r SumSCRRB_SCequalRRB_Disc.t
## IC01_IC12 0.7030830 0.039682918 0.34837115
## IC03_IC13 0.7055483 -0.207728240 -1.51177932
## IC04_IC11 0.7267282 0.049528691 0.93278094
## IC04_IC12 1.0202064 0.057855158 0.28564671
## IC05_IC06 0.5023456 0.080266276 0.09158388
## IC05_IC19 0.7538494 0.237036965 1.47165666
## IC07_IC13 0.6990597 -0.078614861 -0.36252382
## IC07_IC16 0.7858125 -0.007431483 -0.40569269
## IC08_IC11 0.7680143 0.163775449 1.80008504
## IC08_IC20 1.4445651 0.109778625 0.49430368
## IC11_IC12 0.9614569 -0.169326009 -1.17277260
## IC12_IC17 3.1856173 0.133243166 0.97994473
## IC13_IC14 1.9247746 -0.246526697 -1.49077308
## IC14_IC20 0.9854177 0.086094875 0.34126300
## IC15_IC17 2.3089681 0.010249859 -0.04220945
## IC17_IC18 0.5526155 0.136020835 0.19633520
## SumSCRRB_SCequalRRB_Disc.pval SumSCRRB_SCequalRRB_Rep.r
## IC01_IC12 0.72856415 -0.053275570
## IC03_IC13 0.13490716 -0.066990666
## IC04_IC11 0.35400805 -0.060997222
## IC04_IC12 0.77595768 -0.214238350
## IC05_IC06 0.92727951 -0.044488707
## IC05_IC19 0.14541166 -0.004206571
## IC07_IC13 0.71800810 0.128479115
## IC07_IC16 0.68615507 -0.043633285
## IC08_IC11 0.07597868 0.235325204
## IC08_IC20 0.62257597 0.225446130
## IC11_IC12 0.24469978 -0.219868936
## IC12_IC17 0.33034995 -0.228755573
## IC13_IC14 0.14032965 0.321690324
## IC14_IC20 0.73388593 -0.221042415
## IC15_IC17 0.96644696 0.144977010
## IC17_IC18 0.84489336 -0.136682732
## SumSCRRB_SCequalRRB_Rep.t SumSCRRB_SCequalRRB_Rep.pval
## IC01_IC12 -0.03895442 0.969026260
## IC03_IC13 -0.53514792 0.594070027
## IC04_IC11 -0.11922934 0.905400209
## IC04_IC12 -1.61758166 0.109790015
## IC05_IC06 -0.36941948 0.712816095
## IC05_IC19 -0.07250088 0.942388897
## IC07_IC13 0.95691508 0.341566696
## IC07_IC16 -0.54667412 0.586163160
## IC08_IC11 1.71806861 0.089751588
## IC08_IC20 1.62206712 0.108824438
## IC11_IC12 -1.68178233 0.096610878
## IC12_IC17 -1.44404327 0.152731648
## IC13_IC14 2.76041435 0.007192758
## IC14_IC20 -1.68507731 0.095970869
## IC15_IC17 0.94662057 0.346755585
## IC17_IC18 -0.75618949 0.451813426
## SumSCRRB_SCequalRRB.repBF zds_SCequalRRB_Disc.r zds_SCequalRRB_Disc.t
## IC01_IC12 0.6745920 -0.277097910 -2.35339866
## IC03_IC13 0.6301976 0.045777149 0.52417209
## IC04_IC11 0.5331019 -0.067843384 -0.98272177
## IC04_IC12 1.0552972 0.005012945 0.10919400
## IC05_IC06 0.7119774 -0.075535981 -0.29879596
## IC05_IC19 0.3825588 0.081871832 0.61226453
## IC07_IC13 0.7189493 -0.122625417 -1.18731960
## IC07_IC16 0.8100174 0.132853464 1.45896946
## IC08_IC11 3.0247951 -0.015949137 -0.49102809
## IC08_IC20 1.9093969 0.172856818 2.07638632
## IC11_IC12 2.6949289 0.092674386 0.63635602
## IC12_IC17 0.4622104 -0.174141875 -1.26314580
## IC13_IC14 0.3513133 -0.281095151 -2.31704245
## IC14_IC20 1.0462919 0.066592468 0.56560023
## IC15_IC17 0.8632137 0.012884871 0.02229822
## IC17_IC18 0.7449407 0.020846790 0.74653486
## zds_SCequalRRB_Disc.pval zds_SCequalRRB_Rep.r zds_SCequalRRB_Rep.t
## IC01_IC12 0.02129821 0.01222164 -0.1439936
## IC03_IC13 0.60174683 -0.06574081 -0.4407915
## IC04_IC11 0.32899026 0.01496057 -0.1160502
## IC04_IC12 0.91334822 0.02898635 0.4189487
## IC05_IC06 0.76594467 0.03980576 0.2586047
## IC05_IC19 0.54226522 0.03859306 0.4147816
## IC07_IC13 0.23895077 0.09251081 0.6778012
## IC07_IC16 0.14886315 0.26400831 2.5220961
## IC08_IC11 0.62487955 0.06642119 0.8069976
## IC08_IC20 0.04137936 -0.07261177 -0.5923550
## IC11_IC12 0.52653471 0.07345652 0.8767629
## IC12_IC17 0.21055635 -0.25547619 -2.4806166
## IC13_IC14 0.02330946 0.04072736 0.2928297
## IC14_IC20 0.57339998 0.09615702 0.7012499
## IC15_IC17 0.98227091 -0.28381070 -2.5616960
## IC17_IC18 0.45774195 -0.16083034 -1.6652398
## zds_SCequalRRB_Rep.pval zds_SCequalRRB.repBF zds_SCoverRRB_Disc.r
## IC01_IC12 0.88587691 0.2054507 0.14054072
## IC03_IC13 0.66058325 0.6102065 -0.34326125
## IC04_IC11 0.90791104 0.5822046 0.18211389
## IC04_IC12 0.67640506 0.7476418 -0.09961334
## IC05_IC06 0.79662149 0.6694213 -0.15658841
## IC05_IC19 0.67944038 0.7548410 -0.20957605
## IC07_IC13 0.49990344 0.3685000 0.53192645
## IC07_IC16 0.01370250 12.2238649 0.04521700
## IC08_IC11 0.42212180 0.6386348 -0.27888369
## IC08_IC20 0.55532623 0.1392755 0.05261409
## IC11_IC12 0.38330777 1.0154272 -0.11466004
## IC12_IC17 0.01526879 10.1767698 -0.53560195
## IC13_IC14 0.77042993 0.1313312 0.05823789
## IC14_IC20 0.48523399 0.8925094 0.14695973
## IC15_IC17 0.01234372 3.4568789 -0.23401579
## IC17_IC18 0.09987684 0.6589320 0.10168553
## zds_SCoverRRB_Disc.t zds_SCoverRRB_Disc.pval zds_SCoverRRB_Rep.r
## IC01_IC12 0.794771650 0.4314369522 -0.0060153582
## IC03_IC13 -2.183966561 0.0348879192 -0.0749572171
## IC04_IC11 1.430845263 0.1602410613 0.0633555175
## IC04_IC12 -0.341021583 0.7348721727 -0.0044682949
## IC05_IC06 -0.759347862 0.4520965008 0.0519226751
## IC05_IC19 -1.060219299 0.2954049687 -0.0004208548
## IC07_IC13 3.575314279 0.0009321468 0.0557533273
## IC07_IC16 0.002796665 0.9977824883 0.0558002703
## IC08_IC11 -1.633811953 0.1101459505 -0.1777350289
## IC08_IC20 0.178470459 0.8592543754 -0.0184373130
## IC11_IC12 -0.455418316 0.6512709114 -0.0075125849
## IC12_IC17 -3.624413065 0.0008085294 -0.1655617227
## IC13_IC14 0.132306124 0.8954054759 -0.1726519419
## IC14_IC20 0.959712817 0.3429652836 -0.1854524594
## IC15_IC17 -1.764187190 0.0853366697 -0.4151432130
## IC17_IC18 0.600416678 0.5516154764 -0.1505652622
## zds_SCoverRRB_Rep.t zds_SCoverRRB_Rep.pval zds_SCoverRRB.repBF
## IC01_IC12 -0.18371738 0.85516334 0.55898342
## IC03_IC13 -0.58103842 0.56447437 0.42907591
## IC04_IC11 0.30987127 0.75826752 0.53190112
## IC04_IC12 -0.01676234 0.98670955 0.68111452
## IC05_IC06 0.47240609 0.63920430 0.53475443
## IC05_IC19 -0.08265380 0.93453906 0.55014177
## IC07_IC13 0.46076194 0.64746484 0.06811246
## IC07_IC16 0.49752505 0.62154294 0.74628094
## IC08_IC11 -1.25012638 0.21851790 1.46137709
## IC08_IC20 -0.07952432 0.93701212 0.69110033
## IC11_IC12 0.04006744 0.96823868 0.65812655
## IC12_IC17 -0.97548258 0.33518372 0.19293903
## IC13_IC14 -1.14337452 0.25968014 0.90266897
## IC14_IC20 -1.02958799 0.30938917 0.44372222
## IC15_IC17 -2.59716095 0.01308996 16.31276435
## IC17_IC18 -0.84909502 0.40088167 0.59413399
## VinelandABC_Disc.r VinelandABC_Disc.t VinelandABC_Disc.pval
## IC01_IC12 0.117928538 1.28839305 0.20008779
## IC03_IC13 -0.134772608 -1.21171075 0.22800372
## IC04_IC11 -0.103254277 -1.19071242 0.23611656
## IC04_IC12 0.002441248 0.09422101 0.92509074
## IC05_IC06 0.057668657 0.34139602 0.73340254
## IC05_IC19 -0.143747879 -1.51313280 0.13287524
## IC07_IC13 0.073694498 0.64281122 0.52157295
## IC07_IC16 0.029103565 0.47801451 0.63350953
## IC08_IC11 -0.041406876 -0.28936272 0.77280316
## IC08_IC20 -0.012128605 -0.05912888 0.95294781
## IC11_IC12 0.056106901 0.59532805 0.55274527
## IC12_IC17 0.016795507 0.21690377 0.82865168
## IC13_IC14 0.025951362 0.40944604 0.68294229
## IC14_IC20 -0.182685897 -2.06849525 0.04074162
## IC15_IC17 -0.025714298 -0.44972704 0.65371858
## IC17_IC18 -0.162183284 -1.71739838 0.08848605
## VinelandABC_Rep.r VinelandABC_Rep.t VinelandABC_Rep.pval
## IC01_IC12 0.17706471 1.8423656 0.06779140
## IC03_IC13 0.07548509 0.9979447 0.32023409
## IC04_IC11 -0.11207528 -1.2553148 0.21170617
## IC04_IC12 -0.13179877 -1.2248855 0.22292159
## IC05_IC06 0.03961356 0.3103404 0.75681897
## IC05_IC19 -0.21193640 -2.2455797 0.02648763
## IC07_IC13 -0.04636066 -0.6413866 0.52244583
## IC07_IC16 -0.01998481 -0.1752815 0.86114208
## IC08_IC11 0.00709269 0.3005463 0.76425978
## IC08_IC20 -0.21462466 -2.4143802 0.01721091
## IC11_IC12 0.05621054 0.7092251 0.47950555
## IC12_IC17 0.05329079 0.3791170 0.70524422
## IC13_IC14 0.02675679 0.3433432 0.73191706
## IC14_IC20 -0.21801342 -2.5502905 0.01197059
## IC15_IC17 -0.09745959 -1.2025739 0.23141424
## IC17_IC18 -0.06527082 -0.8748833 0.38331532
## VinelandABC.repBF VinelandABC_SCequalRRB_Disc.r
## IC01_IC12 3.5202257 0.14464438
## IC03_IC13 0.3407267 -0.19066515
## IC04_IC11 1.5352527 -0.10451093
## IC04_IC12 0.9658800 -0.07515700
## IC05_IC06 0.7347593 0.12988458
## IC05_IC19 7.4442016 -0.21402719
## IC07_IC13 0.5696654 0.11911550
## IC07_IC16 0.6392246 -0.12561640
## IC08_IC11 0.6716700 -0.29797757
## IC08_IC20 3.2101596 -0.10977147
## IC11_IC12 0.8988975 -0.14229878
## IC12_IC17 0.7474660 -0.11495606
## IC13_IC14 0.7420257 0.01571556
## IC14_IC20 16.1724744 -0.26680425
## IC15_IC17 1.2591897 0.07376916
## IC17_IC18 0.8510854 -0.09863034
## VinelandABC_SCequalRRB_Disc.t VinelandABC_SCequalRRB_Disc.pval
## IC01_IC12 0.9824985 0.32909942
## IC03_IC13 -1.2045644 0.23226235
## IC04_IC11 -0.8232033 0.41307359
## IC04_IC12 -0.4946500 0.62233264
## IC05_IC06 0.7821750 0.43664151
## IC05_IC19 -1.6285163 0.10772391
## IC07_IC13 0.6710248 0.50432242
## IC07_IC16 -0.5420238 0.58945283
## IC08_IC11 -2.1541049 0.03453314
## IC08_IC20 -0.5812687 0.56284984
## IC11_IC12 -1.0911793 0.27878247
## IC12_IC17 -0.6009245 0.54975165
## IC13_IC14 0.3515030 0.72622361
## IC14_IC20 -2.0528896 0.04366782
## IC15_IC17 0.2685229 0.78905427
## IC17_IC18 -0.7449358 0.45870215
## VinelandABC_SCequalRRB_Rep.r VinelandABC_SCequalRRB_Rep.t
## IC01_IC12 0.0009291509 -0.10969434
## IC03_IC13 -0.0221926870 -0.01260826
## IC04_IC11 0.0961412273 0.66337716
## IC04_IC12 -0.1485553903 -1.00402502
## IC05_IC06 0.1875840886 1.45085120
## IC05_IC19 -0.3046567090 -2.60968217
## IC07_IC13 -0.0787165021 -0.81352967
## IC07_IC16 0.0383986453 0.43953326
## IC08_IC11 0.1036129040 1.12949430
## IC08_IC20 -0.3571061284 -3.22372162
## IC11_IC12 0.0684858666 0.89259738
## IC12_IC17 0.0574014139 0.39808593
## IC13_IC14 0.0914518972 0.91743478
## IC14_IC20 -0.2651582112 -2.41475934
## IC15_IC17 -0.0624815497 -0.77287710
## IC17_IC18 -0.0708768645 -0.77256392
## VinelandABC_SCequalRRB_Rep.pval VinelandABC_SCequalRRB.repBF
## IC01_IC12 0.912933526 0.52046990
## IC03_IC13 0.989972521 0.48753775
## IC04_IC11 0.509044927 0.50214093
## IC04_IC12 0.318471172 1.09199286
## IC05_IC06 0.150831159 1.80317495
## IC05_IC19 0.010860882 15.81255120
## IC07_IC13 0.418390930 0.56243403
## IC07_IC16 0.661490557 0.60558343
## IC08_IC11 0.262151007 0.09098352
## IC08_IC20 0.001848388 20.11427182
## IC11_IC12 0.374818376 0.38903697
## IC12_IC17 0.691654359 0.59046050
## IC13_IC14 0.361742943 0.99009805
## IC14_IC20 0.018087248 11.96639023
## IC15_IC17 0.441930920 0.72343255
## IC17_IC18 0.442115217 0.94455160
## VinelandABC_SCoverRRB_Disc.r VinelandABC_SCoverRRB_Disc.t
## IC01_IC12 0.15885195 1.0367605
## IC03_IC13 -0.08587292 -0.4067815
## IC04_IC11 -0.11135463 -0.6644157
## IC04_IC12 0.08946024 0.6635882
## IC05_IC06 -0.02039252 -0.1207941
## IC05_IC19 -0.10210360 -0.4131089
## IC07_IC13 0.08133614 0.2642324
## IC07_IC16 0.18265234 1.1511466
## IC08_IC11 0.20768412 1.5710969
## IC08_IC20 0.09966423 0.4156872
## IC11_IC12 0.27610173 2.0077711
## IC12_IC17 0.17458268 1.0564789
## IC13_IC14 0.01658595 0.2251281
## IC14_IC20 -0.03275164 -0.3627840
## IC15_IC17 -0.08710577 -0.6114066
## IC17_IC18 -0.28114540 -1.9294988
## VinelandABC_SCoverRRB_Disc.pval VinelandABC_SCoverRRB_Rep.r
## IC01_IC12 0.30607461 0.540836261
## IC03_IC13 0.68633693 0.172613120
## IC04_IC11 0.51023658 -0.454390194
## IC04_IC12 0.51076065 -0.126702425
## IC05_IC06 0.90445884 -0.156913702
## IC05_IC19 0.68173309 -0.069523596
## IC07_IC13 0.79295754 -0.012187289
## IC07_IC16 0.25650672 -0.159445476
## IC08_IC11 0.12403778 -0.189792284
## IC08_IC20 0.67986057 0.124610150
## IC11_IC12 0.05145535 -0.033979460
## IC12_IC17 0.29708868 -0.004087625
## IC13_IC14 0.82302587 -0.140739306
## IC14_IC20 0.71867627 -0.054085220
## IC15_IC17 0.54438979 -0.159547623
## IC17_IC18 0.06078353 -0.054271484
## VinelandABC_SCoverRRB_Rep.t VinelandABC_SCoverRRB_Rep.pval
## IC01_IC12 3.901955299 0.0003568259
## IC03_IC13 1.278292374 0.2085142873
## IC04_IC11 -3.099449158 0.0035422004
## IC04_IC12 -0.755126483 0.4545966129
## IC05_IC06 -1.074770171 0.2889182338
## IC05_IC19 -0.230339680 0.8190018227
## IC07_IC13 -0.156498546 0.8764272855
## IC07_IC16 -1.212629240 0.2323850711
## IC08_IC11 -1.041616360 0.3038445676
## IC08_IC20 0.673357137 0.5045923698
## IC11_IC12 -0.166130682 0.8688910085
## IC12_IC17 -0.001428922 0.9988669887
## IC13_IC14 -0.820378260 0.4168627071
## IC14_IC20 -0.406499341 0.6865425470
## IC15_IC17 -1.079280129 0.2869280780
## IC17_IC18 -0.502289832 0.6182177344
## VinelandABC_SCoverRRB.repBF
## IC01_IC12 140.8582916
## IC03_IC13 0.7860256
## IC04_IC11 17.9491075
## IC04_IC12 0.5646209
## IC05_IC06 0.9982184
## IC05_IC19 0.7129276
## IC07_IC13 0.6780060
## IC07_IC16 0.3612854
## IC08_IC11 0.2193664
## IC08_IC20 0.8661881
## IC11_IC12 0.2148602
## IC12_IC17 0.5261220
## IC13_IC14 0.7469726
## IC14_IC20 0.7600222
## IC15_IC17 1.1920280
## IC17_IC18 0.4702474
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask5 = aovres$SCcorr.repBF>=10
mask6 = aovres$RRBcorr.repBF>=10
mask7 = aovres$SumSCRRB.repBF>=10
mask8 = aovres$zds.repBF>=10
mask9 = aovres$zds_SCequalRRB.repBF>=10
mask10 = aovres$zds_SCoverRRB.repBF>=10
mask11 = aovres$SumSCRRB_SCequalRRB.repBF>=10
mask12 = aovres$VinelandABC.repBF>=10
mask13 = aovres$VinelandABC_SCequalRRB.repBF>=10
mask14 = aovres$VinelandABC_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4 | mask5 | mask6 | mask7 | mask8 | mask9 | mask10 | mask11 | mask12 | mask13 | mask14
aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF",
"SCcorr_Disc.r","SCcorr_Rep.r","SCcorr_Disc.pval","SCcorr_Rep.pval","SCcorr.repBF",
"RRBcorr_Disc.r","RRBcorr_Rep.r","RRBcorr_Disc.pval","RRBcorr_Rep.pval","RRBcorr.repBF",
"SumSCRRB_Disc.r","SumSCRRB_Rep.r","SumSCRRB_Disc.pval","SumSCRRB_Rep.pval","SumSCRRB.repBF",
"zds_Disc.r","zds_Rep.r","zds_Disc.pval","zds_Rep.pval","zds.repBF",
"SumSCRRB_SCequalRRB_Disc.r","SumSCRRB_SCequalRRB_Rep.r",
"SumSCRRB_SCequalRRB_Disc.pval","SumSCRRB_SCequalRRB_Rep.pval","SumSCRRB_SCequalRRB.repBF",
"zds_SCequalRRB_Disc.r","zds_SCequalRRB_Rep.r",
"zds_SCequalRRB_Disc.pval","zds_SCequalRRB_Rep.pval","zds_SCequalRRB.repBF",
"zds_SCoverRRB_Disc.r","zds_SCoverRRB_Rep.r",
"zds_SCoverRRB_Disc.pval","zds_SCoverRRB_Rep.pval","zds_SCoverRRB.repBF",
"VinelandABC_Disc.r","VinelandABC_Rep.r",
"VinelandABC_Disc.pval","VinelandABC_Rep.pval","VinelandABC.repBF",
"VinelandABC_SCequalRRB_Disc.r","VinelandABC_SCequalRRB_Rep.r",
"VinelandABC_SCequalRRB_Disc.pval","VinelandABC_SCequalRRB_Rep.pval",
"VinelandABC_SCequalRRB.repBF",
"VinelandABC_SCoverRRB_Disc.r","VinelandABC_SCoverRRB_Rep.r",
"VinelandABC_SCoverRRB_Disc.pval","VinelandABC_SCoverRRB_Rep.pval",
"VinelandABC_SCoverRRB.repBF")]
## compNames SCequalRRB.repBF SCoverRRB.repBF SCcorr_Disc.r SCcorr_Rep.r
## IC01_IC12 IC01_IC12 0.7955174 0.4690143 0.02194196 0.003233085
## IC03_IC13 IC03_IC13 14.0854368 6.9485035 -0.21565303 -0.126963611
## IC04_IC11 IC04_IC11 0.7314191 0.7257347 0.05368490 -0.018351087
## IC04_IC12 IC04_IC12 16.1957454 0.5209997 -0.04308262 0.008786831
## IC05_IC06 IC05_IC06 80.0450180 1.4328289 -0.02776040 0.009072133
## IC05_IC19 IC05_IC19 0.8767820 1.8687764 0.08944708 -0.036191267
## IC07_IC13 IC07_IC13 737.3305480 9.8489366 0.02825143 0.056376570
## IC07_IC16 IC07_IC16 0.3237813 0.6219226 0.04398889 -0.018554199
## IC08_IC11 IC08_IC11 0.6878668 0.6957798 0.11614033 0.187959425
## IC08_IC20 IC08_IC20 1.4262907 0.6610975 0.06102096 0.200556463
## IC11_IC12 IC11_IC12 0.6891131 13.3782336 -0.09722395 -0.124975082
## IC12_IC17 IC12_IC17 4.5013271 11.5978596 -0.09815559 -0.203831211
## IC13_IC14 IC13_IC14 393.9080616 0.3826019 -0.05987545 -0.016340357
## IC14_IC20 IC14_IC20 12.5596603 2.0574500 0.10943481 -0.034469408
## IC15_IC17 IC15_IC17 0.4119129 0.3687863 0.03926970 -0.068463813
## IC17_IC18 IC17_IC18 16.2852363 1.4573308 0.12777685 -0.097275106
## SCcorr_Disc.pval SCcorr_Rep.pval SCcorr.repBF RRBcorr_Disc.r
## IC01_IC12 0.6703983 0.87737433 0.6948175 0.022253885
## IC03_IC13 0.0334581 0.26358581 0.9932646 -0.100984042
## IC04_IC11 0.3378064 0.85020459 0.5092501 0.001223994
## IC04_IC12 0.7486846 0.65673363 0.6679560 -0.018878034
## IC05_IC06 0.5858669 0.88893298 0.6784365 0.129883126
## IC05_IC19 0.3018139 0.73140964 0.4593061 0.111258537
## IC07_IC13 0.7955791 0.78982547 0.7257912 0.006504303
## IC07_IC16 0.7642994 0.84887051 0.6716151 0.046963448
## IC08_IC11 0.1102083 0.02476101 8.0951621 0.140225485
## IC08_IC20 0.6297706 0.04189683 3.1180687 0.004305451
## IC11_IC12 0.3404468 0.21671088 1.4839549 -0.065262479
## IC12_IC17 0.2629685 0.02214415 7.0608340 0.268013929
## IC13_IC14 0.6899370 0.93721109 0.6627342 -0.033549747
## IC14_IC20 0.4673248 0.50302870 0.5366952 0.059448943
## IC15_IC17 0.9364592 0.28002795 0.9027455 0.059630717
## IC17_IC18 0.2831826 0.23107323 0.3932325 0.066930613
## RRBcorr_Rep.r RRBcorr_Disc.pval RRBcorr_Rep.pval RRBcorr.repBF
## IC01_IC12 -0.031570729 0.67581534 0.662192433 0.7714689
## IC03_IC13 -0.105545423 0.32717839 0.330239493 1.1275916
## IC04_IC11 -0.006080479 0.39683381 0.489930085 0.8842338
## IC04_IC12 -0.132022831 0.91914190 0.201782973 1.1336710
## IC05_IC06 0.008273737 0.30770101 0.990641170 0.5386826
## IC05_IC19 0.020045801 0.23452312 0.904463122 0.5254590
## IC07_IC13 0.080865498 0.85975739 0.704722665 0.7454881
## IC07_IC16 -0.065311241 0.99053057 0.283640056 0.9349332
## IC08_IC11 0.234578549 0.08988736 0.022725465 8.8949014
## IC08_IC20 0.135383657 0.80035822 0.252352808 0.8340661
## IC11_IC12 -0.029488716 0.54328656 0.571609043 0.8210033
## IC12_IC17 -0.111746204 0.03543136 0.471909306 0.1191160
## IC13_IC14 0.240763309 0.94254345 0.005337477 4.5660604
## IC14_IC20 -0.196980403 0.89606118 0.038726525 1.8112695
## IC15_IC17 0.181375760 0.80383217 0.131115427 1.4936044
## IC17_IC18 -0.131431759 0.82941025 0.227572306 0.8830450
## SumSCRRB_Disc.r SumSCRRB_Rep.r SumSCRRB_Disc.pval SumSCRRB_Rep.pval
## IC01_IC12 0.02901956 -0.01216495 0.59639342 0.744267295
## IC03_IC13 -0.21386179 -0.14306998 0.05019802 0.205371003
## IC04_IC11 0.03906505 -0.01646374 0.27528630 0.921735250
## IC04_IC12 -0.04208466 -0.05450550 0.78758276 0.723083828
## IC05_IC06 0.05825782 0.01057013 0.82961723 0.926953069
## IC05_IC19 0.13048683 -0.01768556 0.18012260 0.853706029
## IC07_IC13 0.02407416 0.07926642 0.78434455 0.712548753
## IC07_IC16 0.05957621 -0.04395750 0.82602984 0.540215720
## IC08_IC11 0.16676443 0.24768499 0.04649155 0.007703423
## IC08_IC20 0.04615055 0.21130671 0.86148346 0.044697063
## IC11_IC12 -0.10848355 -0.10658917 0.32474187 0.261342137
## IC12_IC17 0.09204190 -0.20289733 0.63770172 0.055301730
## IC13_IC14 -0.06286582 0.09966974 0.75457845 0.144709887
## IC14_IC20 0.11370131 -0.11684976 0.59080811 0.141479102
## IC15_IC17 0.06380923 0.03307955 0.82805483 0.970847557
## IC17_IC18 0.13122882 -0.13299202 0.38186762 0.159515783
## SumSCRRB.repBF zds_Disc.r zds_Rep.r zds_Disc.pval zds_Rep.pval
## IC01_IC12 0.7302895 0.002657793 0.023309478 0.978621128 0.87424447
## IC03_IC13 1.3723820 -0.107756293 -0.062880426 0.320029498 0.66763968
## IC04_IC11 0.5458223 0.043233647 -0.014899531 0.876162830 0.55813481
## IC04_IC12 0.7446164 -0.022345781 0.092781160 0.845957187 0.15265506
## IC05_IC06 0.6864205 -0.112569212 0.004027948 0.193913203 0.88122595
## IC05_IC19 0.3920961 -0.003416208 -0.049681652 0.908621815 0.65702061
## IC07_IC13 0.7487993 0.018705713 0.006338976 0.934075525 0.99565767
## IC07_IC16 0.7123406 0.003703727 0.022444169 0.787040843 0.61519338
## IC08_IC11 22.7349100 -0.001563469 0.042788914 0.967658120 0.52097367
## IC08_IC20 2.3254318 0.047133160 0.119371439 0.543994106 0.19797173
## IC11_IC12 1.3174932 -0.034806562 -0.109081175 0.735765943 0.38135921
## IC12_IC17 1.0794209 -0.266888039 -0.137767387 0.005822396 0.06242402
## IC13_IC14 0.9382909 -0.026012431 -0.170165849 0.778505391 0.08031152
## IC14_IC20 0.7619345 0.048866197 0.090168120 0.588162711 0.39621295
## IC15_IC17 0.6886295 -0.008920903 -0.185111864 0.909308101 0.03988013
## IC17_IC18 0.5152166 0.058791490 -0.015996310 0.462311380 0.72393577
## zds.repBF SumSCRRB_SCequalRRB_Disc.r SumSCRRB_SCequalRRB_Rep.r
## IC01_IC12 0.7030830 0.039682918 -0.053275570
## IC03_IC13 0.7055483 -0.207728240 -0.066990666
## IC04_IC11 0.7267282 0.049528691 -0.060997222
## IC04_IC12 1.0202064 0.057855158 -0.214238350
## IC05_IC06 0.5023456 0.080266276 -0.044488707
## IC05_IC19 0.7538494 0.237036965 -0.004206571
## IC07_IC13 0.6990597 -0.078614861 0.128479115
## IC07_IC16 0.7858125 -0.007431483 -0.043633285
## IC08_IC11 0.7680143 0.163775449 0.235325204
## IC08_IC20 1.4445651 0.109778625 0.225446130
## IC11_IC12 0.9614569 -0.169326009 -0.219868936
## IC12_IC17 3.1856173 0.133243166 -0.228755573
## IC13_IC14 1.9247746 -0.246526697 0.321690324
## IC14_IC20 0.9854177 0.086094875 -0.221042415
## IC15_IC17 2.3089681 0.010249859 0.144977010
## IC17_IC18 0.5526155 0.136020835 -0.136682732
## SumSCRRB_SCequalRRB_Disc.pval SumSCRRB_SCequalRRB_Rep.pval
## IC01_IC12 0.72856415 0.969026260
## IC03_IC13 0.13490716 0.594070027
## IC04_IC11 0.35400805 0.905400209
## IC04_IC12 0.77595768 0.109790015
## IC05_IC06 0.92727951 0.712816095
## IC05_IC19 0.14541166 0.942388897
## IC07_IC13 0.71800810 0.341566696
## IC07_IC16 0.68615507 0.586163160
## IC08_IC11 0.07597868 0.089751588
## IC08_IC20 0.62257597 0.108824438
## IC11_IC12 0.24469978 0.096610878
## IC12_IC17 0.33034995 0.152731648
## IC13_IC14 0.14032965 0.007192758
## IC14_IC20 0.73388593 0.095970869
## IC15_IC17 0.96644696 0.346755585
## IC17_IC18 0.84489336 0.451813426
## SumSCRRB_SCequalRRB.repBF zds_SCequalRRB_Disc.r zds_SCequalRRB_Rep.r
## IC01_IC12 0.6745920 -0.277097910 0.01222164
## IC03_IC13 0.6301976 0.045777149 -0.06574081
## IC04_IC11 0.5331019 -0.067843384 0.01496057
## IC04_IC12 1.0552972 0.005012945 0.02898635
## IC05_IC06 0.7119774 -0.075535981 0.03980576
## IC05_IC19 0.3825588 0.081871832 0.03859306
## IC07_IC13 0.7189493 -0.122625417 0.09251081
## IC07_IC16 0.8100174 0.132853464 0.26400831
## IC08_IC11 3.0247951 -0.015949137 0.06642119
## IC08_IC20 1.9093969 0.172856818 -0.07261177
## IC11_IC12 2.6949289 0.092674386 0.07345652
## IC12_IC17 0.4622104 -0.174141875 -0.25547619
## IC13_IC14 0.3513133 -0.281095151 0.04072736
## IC14_IC20 1.0462919 0.066592468 0.09615702
## IC15_IC17 0.8632137 0.012884871 -0.28381070
## IC17_IC18 0.7449407 0.020846790 -0.16083034
## zds_SCequalRRB_Disc.pval zds_SCequalRRB_Rep.pval zds_SCequalRRB.repBF
## IC01_IC12 0.02129821 0.88587691 0.2054507
## IC03_IC13 0.60174683 0.66058325 0.6102065
## IC04_IC11 0.32899026 0.90791104 0.5822046
## IC04_IC12 0.91334822 0.67640506 0.7476418
## IC05_IC06 0.76594467 0.79662149 0.6694213
## IC05_IC19 0.54226522 0.67944038 0.7548410
## IC07_IC13 0.23895077 0.49990344 0.3685000
## IC07_IC16 0.14886315 0.01370250 12.2238649
## IC08_IC11 0.62487955 0.42212180 0.6386348
## IC08_IC20 0.04137936 0.55532623 0.1392755
## IC11_IC12 0.52653471 0.38330777 1.0154272
## IC12_IC17 0.21055635 0.01526879 10.1767698
## IC13_IC14 0.02330946 0.77042993 0.1313312
## IC14_IC20 0.57339998 0.48523399 0.8925094
## IC15_IC17 0.98227091 0.01234372 3.4568789
## IC17_IC18 0.45774195 0.09987684 0.6589320
## zds_SCoverRRB_Disc.r zds_SCoverRRB_Rep.r zds_SCoverRRB_Disc.pval
## IC01_IC12 0.14054072 -0.0060153582 0.4314369522
## IC03_IC13 -0.34326125 -0.0749572171 0.0348879192
## IC04_IC11 0.18211389 0.0633555175 0.1602410613
## IC04_IC12 -0.09961334 -0.0044682949 0.7348721727
## IC05_IC06 -0.15658841 0.0519226751 0.4520965008
## IC05_IC19 -0.20957605 -0.0004208548 0.2954049687
## IC07_IC13 0.53192645 0.0557533273 0.0009321468
## IC07_IC16 0.04521700 0.0558002703 0.9977824883
## IC08_IC11 -0.27888369 -0.1777350289 0.1101459505
## IC08_IC20 0.05261409 -0.0184373130 0.8592543754
## IC11_IC12 -0.11466004 -0.0075125849 0.6512709114
## IC12_IC17 -0.53560195 -0.1655617227 0.0008085294
## IC13_IC14 0.05823789 -0.1726519419 0.8954054759
## IC14_IC20 0.14695973 -0.1854524594 0.3429652836
## IC15_IC17 -0.23401579 -0.4151432130 0.0853366697
## IC17_IC18 0.10168553 -0.1505652622 0.5516154764
## zds_SCoverRRB_Rep.pval zds_SCoverRRB.repBF VinelandABC_Disc.r
## IC01_IC12 0.85516334 0.55898342 0.117928538
## IC03_IC13 0.56447437 0.42907591 -0.134772608
## IC04_IC11 0.75826752 0.53190112 -0.103254277
## IC04_IC12 0.98670955 0.68111452 0.002441248
## IC05_IC06 0.63920430 0.53475443 0.057668657
## IC05_IC19 0.93453906 0.55014177 -0.143747879
## IC07_IC13 0.64746484 0.06811246 0.073694498
## IC07_IC16 0.62154294 0.74628094 0.029103565
## IC08_IC11 0.21851790 1.46137709 -0.041406876
## IC08_IC20 0.93701212 0.69110033 -0.012128605
## IC11_IC12 0.96823868 0.65812655 0.056106901
## IC12_IC17 0.33518372 0.19293903 0.016795507
## IC13_IC14 0.25968014 0.90266897 0.025951362
## IC14_IC20 0.30938917 0.44372222 -0.182685897
## IC15_IC17 0.01308996 16.31276435 -0.025714298
## IC17_IC18 0.40088167 0.59413399 -0.162183284
## VinelandABC_Rep.r VinelandABC_Disc.pval VinelandABC_Rep.pval
## IC01_IC12 0.17706471 0.20008779 0.06779140
## IC03_IC13 0.07548509 0.22800372 0.32023409
## IC04_IC11 -0.11207528 0.23611656 0.21170617
## IC04_IC12 -0.13179877 0.92509074 0.22292159
## IC05_IC06 0.03961356 0.73340254 0.75681897
## IC05_IC19 -0.21193640 0.13287524 0.02648763
## IC07_IC13 -0.04636066 0.52157295 0.52244583
## IC07_IC16 -0.01998481 0.63350953 0.86114208
## IC08_IC11 0.00709269 0.77280316 0.76425978
## IC08_IC20 -0.21462466 0.95294781 0.01721091
## IC11_IC12 0.05621054 0.55274527 0.47950555
## IC12_IC17 0.05329079 0.82865168 0.70524422
## IC13_IC14 0.02675679 0.68294229 0.73191706
## IC14_IC20 -0.21801342 0.04074162 0.01197059
## IC15_IC17 -0.09745959 0.65371858 0.23141424
## IC17_IC18 -0.06527082 0.08848605 0.38331532
## VinelandABC.repBF VinelandABC_SCequalRRB_Disc.r
## IC01_IC12 3.5202257 0.14464438
## IC03_IC13 0.3407267 -0.19066515
## IC04_IC11 1.5352527 -0.10451093
## IC04_IC12 0.9658800 -0.07515700
## IC05_IC06 0.7347593 0.12988458
## IC05_IC19 7.4442016 -0.21402719
## IC07_IC13 0.5696654 0.11911550
## IC07_IC16 0.6392246 -0.12561640
## IC08_IC11 0.6716700 -0.29797757
## IC08_IC20 3.2101596 -0.10977147
## IC11_IC12 0.8988975 -0.14229878
## IC12_IC17 0.7474660 -0.11495606
## IC13_IC14 0.7420257 0.01571556
## IC14_IC20 16.1724744 -0.26680425
## IC15_IC17 1.2591897 0.07376916
## IC17_IC18 0.8510854 -0.09863034
## VinelandABC_SCequalRRB_Rep.r VinelandABC_SCequalRRB_Disc.pval
## IC01_IC12 0.0009291509 0.32909942
## IC03_IC13 -0.0221926870 0.23226235
## IC04_IC11 0.0961412273 0.41307359
## IC04_IC12 -0.1485553903 0.62233264
## IC05_IC06 0.1875840886 0.43664151
## IC05_IC19 -0.3046567090 0.10772391
## IC07_IC13 -0.0787165021 0.50432242
## IC07_IC16 0.0383986453 0.58945283
## IC08_IC11 0.1036129040 0.03453314
## IC08_IC20 -0.3571061284 0.56284984
## IC11_IC12 0.0684858666 0.27878247
## IC12_IC17 0.0574014139 0.54975165
## IC13_IC14 0.0914518972 0.72622361
## IC14_IC20 -0.2651582112 0.04366782
## IC15_IC17 -0.0624815497 0.78905427
## IC17_IC18 -0.0708768645 0.45870215
## VinelandABC_SCequalRRB_Rep.pval VinelandABC_SCequalRRB.repBF
## IC01_IC12 0.912933526 0.52046990
## IC03_IC13 0.989972521 0.48753775
## IC04_IC11 0.509044927 0.50214093
## IC04_IC12 0.318471172 1.09199286
## IC05_IC06 0.150831159 1.80317495
## IC05_IC19 0.010860882 15.81255120
## IC07_IC13 0.418390930 0.56243403
## IC07_IC16 0.661490557 0.60558343
## IC08_IC11 0.262151007 0.09098352
## IC08_IC20 0.001848388 20.11427182
## IC11_IC12 0.374818376 0.38903697
## IC12_IC17 0.691654359 0.59046050
## IC13_IC14 0.361742943 0.99009805
## IC14_IC20 0.018087248 11.96639023
## IC15_IC17 0.441930920 0.72343255
## IC17_IC18 0.442115217 0.94455160
## VinelandABC_SCoverRRB_Disc.r VinelandABC_SCoverRRB_Rep.r
## IC01_IC12 0.15885195 0.540836261
## IC03_IC13 -0.08587292 0.172613120
## IC04_IC11 -0.11135463 -0.454390194
## IC04_IC12 0.08946024 -0.126702425
## IC05_IC06 -0.02039252 -0.156913702
## IC05_IC19 -0.10210360 -0.069523596
## IC07_IC13 0.08133614 -0.012187289
## IC07_IC16 0.18265234 -0.159445476
## IC08_IC11 0.20768412 -0.189792284
## IC08_IC20 0.09966423 0.124610150
## IC11_IC12 0.27610173 -0.033979460
## IC12_IC17 0.17458268 -0.004087625
## IC13_IC14 0.01658595 -0.140739306
## IC14_IC20 -0.03275164 -0.054085220
## IC15_IC17 -0.08710577 -0.159547623
## IC17_IC18 -0.28114540 -0.054271484
## VinelandABC_SCoverRRB_Disc.pval VinelandABC_SCoverRRB_Rep.pval
## IC01_IC12 0.30607461 0.0003568259
## IC03_IC13 0.68633693 0.2085142873
## IC04_IC11 0.51023658 0.0035422004
## IC04_IC12 0.51076065 0.4545966129
## IC05_IC06 0.90445884 0.2889182338
## IC05_IC19 0.68173309 0.8190018227
## IC07_IC13 0.79295754 0.8764272855
## IC07_IC16 0.25650672 0.2323850711
## IC08_IC11 0.12403778 0.3038445676
## IC08_IC20 0.67986057 0.5045923698
## IC11_IC12 0.05145535 0.8688910085
## IC12_IC17 0.29708868 0.9988669887
## IC13_IC14 0.82302587 0.4168627071
## IC14_IC20 0.71867627 0.6865425470
## IC15_IC17 0.54438979 0.2869280780
## IC17_IC18 0.06078353 0.6182177344
## VinelandABC_SCoverRRB.repBF
## IC01_IC12 140.8582916
## IC03_IC13 0.7860256
## IC04_IC11 17.9491075
## IC04_IC12 0.5646209
## IC05_IC06 0.9982184
## IC05_IC19 0.7129276
## IC07_IC13 0.6780060
## IC07_IC16 0.3612854
## IC08_IC11 0.2193664
## IC08_IC20 0.8661881
## IC11_IC12 0.2148602
## IC12_IC17 0.5261220
## IC13_IC14 0.7469726
## IC14_IC20 0.7600222
## IC15_IC17 1.1920280
## IC17_IC18 0.4702474
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)
SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0
SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0
SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0
SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0
SCcorr_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCcorr_Disc_mat) = comps
colnames(SCcorr_Disc_mat) = comps
diag(SCcorr_Disc_mat) = 0
SCcorr_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCcorr_Rep_mat) = comps
colnames(SCcorr_Rep_mat) = comps
diag(SCcorr_Rep_mat) = 0
RRBcorr_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(RRBcorr_Disc_mat) = comps
colnames(RRBcorr_Disc_mat) = comps
diag(RRBcorr_Disc_mat) = 0
RRBcorr_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(RRBcorr_Rep_mat) = comps
colnames(RRBcorr_Rep_mat) = comps
diag(RRBcorr_Rep_mat) = 0
SumSCRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SumSCRRB_Disc_mat) = comps
colnames(SumSCRRB_Disc_mat) = comps
diag(SumSCRRB_Disc_mat) = 0
SumSCRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SumSCRRB_Rep_mat) = comps
colnames(SumSCRRB_Rep_mat) = comps
diag(SumSCRRB_Rep_mat) = 0
zds_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_Disc_mat) = comps
colnames(zds_Disc_mat) = comps
diag(zds_Disc_mat) = 0
zds_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_Rep_mat) = comps
colnames(zds_Rep_mat) = comps
diag(zds_Rep_mat) = 0
zds_SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_SCequalRRB_Disc_mat) = comps
colnames(zds_SCequalRRB_Disc_mat) = comps
diag(zds_SCequalRRB_Disc_mat) = 0
zds_SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_SCequalRRB_Rep_mat) = comps
colnames(zds_SCequalRRB_Rep_mat) = comps
diag(zds_SCequalRRB_Rep_mat) = 0
zds_SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_SCoverRRB_Disc_mat) = comps
colnames(zds_SCoverRRB_Disc_mat) = comps
diag(zds_SCoverRRB_Disc_mat) = 0
zds_SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_SCoverRRB_Rep_mat) = comps
colnames(zds_SCoverRRB_Rep_mat) = comps
diag(zds_SCoverRRB_Rep_mat) = 0
VinelandABC_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_Disc_mat) = comps
colnames(VinelandABC_Disc_mat) = comps
diag(VinelandABC_Disc_mat) = 0
VinelandABC_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_Rep_mat) = comps
colnames(VinelandABC_Rep_mat) = comps
diag(VinelandABC_Rep_mat) = 0
VinelandABC_SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_SCequalRRB_Disc_mat) = comps
colnames(VinelandABC_SCequalRRB_Disc_mat) = comps
diag(VinelandABC_SCequalRRB_Disc_mat) = 0
VinelandABC_SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_SCequalRRB_Rep_mat) = comps
colnames(VinelandABC_SCequalRRB_Rep_mat) = comps
diag(VinelandABC_SCequalRRB_Rep_mat) = 0
VinelandABC_SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_SCoverRRB_Disc_mat) = comps
colnames(VinelandABC_SCoverRRB_Disc_mat) = comps
diag(VinelandABC_SCoverRRB_Disc_mat) = 0
VinelandABC_SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_SCoverRRB_Rep_mat) = comps
colnames(VinelandABC_SCoverRRB_Rep_mat) = comps
diag(VinelandABC_SCoverRRB_Rep_mat) = 0
for (comp_pair in aovres$compNames){
comp1 = substr(comp_pair,1,4)
comp2 = substr(comp_pair,6,10)
if (aovres[comp_pair,"SCequalRRB.repBF"]>10 &
aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
} else{
SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"SCoverRRB.repBF"]>10 &
aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 &
aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
} else{
SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"SCcorr.repBF"]>10 &
aovres[comp_pair,"SCcorr_Disc.pval"]<0.05 &
aovres[comp_pair,"SCcorr_Rep.pval"]<0.05){
SCcorr_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCcorr_Disc.r"]
SCcorr_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCcorr_Rep.r"]
} else{
SCcorr_Disc_mat[comp1,comp2] = 0.0001
SCcorr_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"RRBcorr.repBF"]>10 &
aovres[comp_pair,"RRBcorr_Disc.pval"]<0.05 &
aovres[comp_pair,"RRBcorr_Rep.pval"]<0.05){
RRBcorr_Disc_mat[comp1,comp2] = aovres[comp_pair,"RRBcorr_Disc.r"]
RRBcorr_Rep_mat[comp1,comp2] = aovres[comp_pair,"RRBcorr_Rep.r"]
} else{
RRBcorr_Disc_mat[comp1,comp2] = 0.0001
RRBcorr_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"SumSCRRB.repBF"]>10 &
aovres[comp_pair,"SumSCRRB_Disc.pval"]<0.05 &
aovres[comp_pair,"SumSCRRB_Rep.pval"]<0.05){
SumSCRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SumSCRRB_Disc.r"]
SumSCRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SumSCRRB_Rep.r"]
} else{
SumSCRRB_Disc_mat[comp1,comp2] = 0.0001
SumSCRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"zds.repBF"]>10 &
aovres[comp_pair,"zds_Disc.pval"]<0.05 &
aovres[comp_pair,"zds_Rep.pval"]<0.05){
zds_Disc_mat[comp1,comp2] = aovres[comp_pair,"zds_Disc.r"]
zds_Rep_mat[comp1,comp2] = aovres[comp_pair,"zds_Rep.r"]
} else{
zds_Disc_mat[comp1,comp2] = 0.0001
zds_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"zds_SCequalRRB.repBF"]>10 &
aovres[comp_pair,"zds_SCequalRRB_Disc.pval"]<0.05 &
aovres[comp_pair,"zds_SCequalRRB_Rep.pval"]<0.05){
zds_SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"zds_SCequalRRB_Disc.r"]
zds_SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"zds_SCequalRRB_Rep.r"]
} else{
zds_SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
zds_SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"zds_SCoverRRB.repBF"]>10 &
aovres[comp_pair,"zds_SCoverRRB_Disc.pval"]<0.05 &
aovres[comp_pair,"zds_SCoverRRB_Rep.pval"]<0.05){
zds_SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"zds_SCoverRRB_Disc.r"]
zds_SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"zds_SCoverRRB_Rep.r"]
} else{
zds_SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
zds_SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"VinelandABC.repBF"]>10 &
aovres[comp_pair,"VinelandABC_Disc.pval"]<0.05 &
aovres[comp_pair,"VinelandABC_Rep.pval"]<0.05){
VinelandABC_Disc_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_Disc.r"]
VinelandABC_Rep_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_Rep.r"]
} else{
VinelandABC_Disc_mat[comp1,comp2] = 0.0001
VinelandABC_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"VinelandABC_SCequalRRB.repBF"]>10 &
aovres[comp_pair,"VinelandABC_SCequalRRB_Disc.pval"]<0.05 &
aovres[comp_pair,"VinelandABC_SCequalRRB_Rep.pval"]<0.05){
VinelandABC_SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_SCequalRRB_Disc.r"]
VinelandABC_SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_SCequalRRB_Rep.r"]
} else{
VinelandABC_SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
VinelandABC_SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
}
if (aovres[comp_pair,"VinelandABC_SCoverRRB.repBF"]>10 &
aovres[comp_pair,"VinelandABC_SCoverRRB_Disc.pval"]<0.05 &
aovres[comp_pair,"VinelandABC_SCoverRRB_Rep.pval"]<0.05){
VinelandABC_SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_SCoverRRB_Disc.r"]
VinelandABC_SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_SCoverRRB_Rep.r"]
} else{
VinelandABC_SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
VinelandABC_SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
}
}
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCcorr_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCcorr_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(RRBcorr_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(RRBcorr_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SumSCRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SumSCRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
geom_tile(aes(fill=freq, alpha=0.5)) +
scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
theme_minimal() +
theme(legend.title = element_blank(),
legend.position = "none",
axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank()) +
coord_flip()
p_cbar
#------------------------------------------------------------------------------
# Consensus Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)
SCequalRRB_consensusPairs = c("IC07_IC13")
SCoverRRB_consensusPairs = c("IC12_IC17")
SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0
SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0
SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0
SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0
for (comp_pair in aovres$compNames){
comp1 = substr(comp_pair,1,4)
comp2 = substr(comp_pair,6,10)
if (is.element(comp_pair,SCequalRRB_consensusPairs)){
SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
} else{
SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
} # if
if (is.element(comp_pair,SCoverRRB_consensusPairs)){
SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
} else{
SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
} # if
} # for
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "green",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "grey",
IC13 = "green",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "grey",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
grid.col = c(IC01 = "grey",
IC03 = "grey",
IC04 = "grey",
IC05 = "grey",
IC06 = "grey",
IC07 = "grey",
IC08 = "grey",
IC09 = "grey",
IC10 = "grey",
IC11 = "grey",
IC12 = "blue",
IC13 = "grey",
IC14 = "grey",
IC15 = "grey",
IC16 = "grey",
IC17 = "blue",
IC18 = "grey",
IC19 = "grey",
IC20 = "grey")
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))
plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
geom_tile(aes(fill=freq, alpha=0.5)) +
scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
theme_minimal() +
theme(legend.title = element_blank(),
legend.position = "none",
axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank()) +
coord_flip()
p_cbar